in imperative and BE *going to* + infinitive constructions. Automatically excluding imperatives proved computationally difficult, but since BE *going to* + infinitive constructions were captured by the CQL query, and manually tagged for separately, they could easily be counted. Thus, this study uses the following F-coefficient (38) ('F' for finite verb phrases) as a comparison measure between textbook and naturally occurring data:

$$ (38) \qquad F = \frac{N_{\text{PROG}}}{(N_{\text{FINITE\_VERB\_PHRASES}} - N_{\text{PROG}} - N_{\text{GOING\_TO}})} \times 10,000 $$

To identify finite verb phrases (hereafter: FVPs), Rautionaho (2014: 78) manually annotated thousands of verb phrases. Though undoubtedly the more accurate method, in the context of this case study, relying on relatively large corpora, it was not a feasible option. However, the availability of fast and fairly robust automatic dependency parsers for English means that it is nowadays possible to automatically retrieve verb phrases on the basis of syntactic dependency relations. Consequently, a combination of POS and syntactic dependencies information was used to approximate the total number of FVPs in each of the corpora under study. To this end, the corpora were tokenised, parsed and tagged using the Python library {spaCy} (Honnibal & Johnson 2015; Honnibal & Montani 2017) via the R interface to Python {reticulate} (Allaire, Ushey & Tang 2019) and the R package {cleanNLP} (Arnold 2017).

First, POS tags were used to identify all verb forms. Second, the syntactic dependency parsing information capturing the relation connecting child to head was used to determine which verbs were the main verbs in FVPs (thus chiefly removing auxiliaries) and to exclude all verb forms from non-finite verb phrases. The R command which was employed to this effect is the following:

(39) `nrow(subset(spacy_parsed_tagged_corpus, upos==‘VERB’ & relation %in% c(‘ROOT’, ‘relcl’, ‘ccomp’, ‘advcl’, ‘conj’)))`

Using this command, the following verb forms would first be retrieved from the paragraph above on the basis of their POS tag: *were*, *used*, *identify*, *capturing*, *connecting*, *was*, *used*, *determine*, *were*, *removing*, *exclude*, *was*, *employed* and *is*. However, only the following would then be counted as a result of their syntactic dependency relation (given in brackets): *used* (none = ROOT), *used* (none = ROOT), *were* (ccomp), *removing* (advcl), *exclude* (conj), *employed* (relcl) and *is* (none = ROOT). Hence, according to this approximation, the above paragraph is said to contain seven FVPs. As the example above demonstrates, this method is far from perfect, in particular due to the non-negligible error rate in the POS-tagging and dependency-parsing processes and the fact that the relations ‘advcl’ and ‘conj’ can cover both tensed and non-tensed verb phrases. Nonetheless, it was found to be the most reliable automatic method to approximate the number of FVPs for the purposes of this study.
### 4.2.3 Collostructional analysis

Collostructional analysis was designed to empirically investigate the interactions between grammatical constructions and the lexemes associated with them (Stefanowitsch & Gries 2003; Gries & Stefanowitsch 2004a; Gries 2015a; 2019a). Gries (2019a: 386) describes the method as an “extension of the notion of collocation”, to be understood in the traditional sense of co-occurrences of two lexical items, but also of words and patterns (see, e.g., Hunston & Francis 2000) and of words and constructions (see, e.g., Goldberg 1995; 2006).

Collostructional analysis takes a construction as a starting point and measures which lexemes are attracted to or repelled by the construction by measuring whether they occur more or less frequently than expected on the basis of the total lexeme count in the corpus under study. The lexemes associated with a particular construction are referred to as the construction's collexemes (Stefanowitsch & Gries 2003: 215). By contrast, linear collocational approaches are usually based on the interpretation of patterns emerging from either the manual inspection of (semi)-automatically extracted concordance lines, or from lists of the node word's most frequent collocates, traditionally operationalised as words or lemmas most frequently found within a user-specified span around the collocation's node word. In such an approach, the semantic relation between the nodes and their collocates are not usually verified (but see, e.g., Uhrig & Proisl 2012 for a method advocating the use of dependency parsing to identify collocations). Collostructional approaches, on the other hand, restrict the semantic exploration to the syntactic frame(s) of the construction(s) under investigation, thus considerably reducing noise. Crucially, they also account for the marginal frequencies of each collexemes within the corpus under study (for demonstrations of the superiority of collostructional methods as compared to raw and basic relative frequency-based approaches, see Gries, Hampe & Schönefeld 2005; 2010; Desagulier 2014: 155–156; Gries 2015a).

Originally, three types of collostructional analyses were proposed (see Gries 2015a for an overview of the extended family of collostructional methods):

a) Simple Collexeme Analysis (Stefanowitsch & Gries 2003) is used to quantify the extent to which individual words/lemmas occurring in a construction are attracted to or repelled by that construction.

b) Co-varying Collexeme Analysis (CovCA) (Gries & Stefanowitsch 2004b; 2004a) attempts to quantify how much words/lemmas in one slot of a construction are attracted to or repelled by words in a second slot of the same construction (e.g., the into-causative: *to trick/force* [verb slot 1] *someone into buying/accepting* [verb slot 2] *something*) (examples from Gries 2019a: 386).

c) Distinctive Collexeme Analysis (Gries & Stefanowitsch 2004a) compares the extent to which words are attracted to two functionally similar constructions.
A fourth type, Key Collostructional Analysis (Gilquin 2012; 2015a; 2015b; 2016a) involves computing a Distinctive Collexeme Analysis, but rather than comparing the association of words/lemmas with two constructions, it is used to compare the association of words/lemmas with one construction across two or more corpora representing different language varieties (Gilquin 2016: 239–250). Gilquin applies this method to compare the phraseological patterns of use of causative constructions with MAKE (2012; 2016) and phrasal verbs (2015b) in native, EFL and ESL learner varieties. Without naming it as such, Deshors (2017) applies a similar method in her study of the progressive in Word Englishes. She extends it by comparing series of CovCAs to assess the association strength not only between individual lemmas and English varieties, but also between semantic domains and varieties.

Unlike simple frequency-based approaches, which generally report high-frequency light verbs as the strongest collocates, collostructional methods often reveal associations with semantically richer lexical verbs (see also Deshors 2017: 265) and can thus provide insights into semantic, functional and pragmatic domains that are attracted to or repelled by specific constructions.

In theory, collostructional analysis may be conducted with any of the many association measures frequently used in collocational research (see Manning & Schütze 1999: Ch. 5; Evert 2005: Ch. 3 for overviews); however, as pointed out in the aforementioned literature, many of these statistics assume normal distributions and homogeneity of variance, which have been shown to be unrealistic assumptions for natural language data (see Evert 2005: 2.3.1). Moreover, most of these measures perform particularly poorly, i.e., either vastly over- or under-estimating the strength of association, when dealing with very infrequent word co-occurrences. This is potentially highly problematic for collostructional analysis because, since construction type/token frequencies have been demonstrated to follow Zipfian distributions (see 1.5), we expect the vast majority of collexemes associated with any construction to occur at very low frequencies (Stefanowitsch & Gries 2003: 218).

In this thesis, collostructional analyses are conducted using Gries's (2019b) {coll_analysis} package for R with the default association measure setting of -log₁₀ Fisher-Yates exact [FYE], one-tailed (except for the comparative DCAs, see 4.2.3.2). FYE test is often hailed as the significant test of choice for co-occurrence contingency tables (Pedersen 1996), in particular when low frequencies and skewed distributions are expected (Evert 2009: 1235–1236). Indeed, FYE does not rely on approximations which may skew results for low-frequency data, nor does it make specific sample size demands. This notwithstanding, FYE is known to be computationally expensive (Evert 2009: 1235); however, the {coll_analysis} version used here (v. 3.5) fixes the issue of infinite p_FYE collostructional strength (CL) values being returned for very strong associations or repulsions (cf. Schmid & Küchenhoff 2013: 537) by extending
the numerical computing abilities of the system running the R script thanks to the
`{Rmpfr}` library (Maechler 2019).

In this chapter, two different collostructional methods are applied. The following
subsection (4.2.3.1) describes how, inspired by Gilquin's method (2012; 2015a; 2015b;
2016a), two types of Co-varying Collexeme Analyses are used to compare, first the
verbs used in the progressive, and second, their semantic domains (as in Deshors
2017) across the two TEC subcorpora and their corresponding reference corpora.
Subsequently, 4.2.3.2 explains how the method is expanded to be able to combine the
advantages of a Key Collostructional Analysis with those of Distinctive
Collostructional Analyses.

4.2.3.1 Co-varying Collexeme Analyses: *Verb/Semantic domain +*
*Corpus*

The first set of Co-varying Collexeme Analyses (thereafter, CovCA) conducted as part of this study contrasts the verb lemmas associated with a single constructional variant, the progressive construction, in two subcorpora of the TEC (Conversation and Fiction) and random samples from the Spoken BNC2014 and the Youth Fiction corpus. The second set of CovCAs focuses on the semantic domains to which these lemmas belong and their associations with the progressive construction.

Whilst some insights can only be gained with a progressive versus non-progressive
alternation investigation (as in 4.2.3.2), the advantage of this single constructional
variant is that phrasal and prepositional verbs can easily be investigated separately.
For instance, it enables us to compare the strength of attraction to the progressive
aspect for the following verbs: LOOK, LOOK AFTER, LOOK AROUND, LOOK FOR, LOOK
FORWARD, LOOK OVER and LOOK UP. For practical reasons, all these verbs are
agglomerated into the verb group LOOK in the context of the distinctive alternation
collostructional analyses (DCA, see 4.2.3.2).

The CovCAs were also carried out using Gries’s (2019b) `{coll_analysis}` 3.5 for R. The input file for the first Conversation CovCA consists of a table of 4,846 rows where each row corresponds to one annotated concordance line featuring a progressive as defined in 4.2.2.3. The table has two columns: one for the verb lemma and the other for the (sub)corpus from which the concordance was retrieved. From this input table, the `{coll_analysis}` script constructs, for each of the 623 unique lemmas that are found in the Conversation progressive dataset, a contingency table like the one displayed in Table 10.
<table><caption>Table 10: Example contingency table for the verb SAY in the first CovCA conducted (see 4.2.3.1)</caption><thead><tr><th></th><th>SAY in the<br>progressive</th><th>Other lemmas<br>in the progressive</th><th>Row totals</th></tr></thead><tbody><tr><td>Textbook<br>Conversation</td><td>29 (a)</td><td>2,394 (b)</td><td>2,423 (a + b)</td></tr><tr><td>Spoken BNC2014<br>sample</td><td>153 (c)</td><td>2,270 (d)</td><td>2,423 (c + d)</td></tr><tr><td>Column totals</td><td>182 (a + c)</td><td>4,664 (b + d)</td><td>4,846 (a + b + c + d)</td></tr></tbody></table>

For each verb, the programme computes a collostructional strength (CL) value which, in these CovCAs, is a measure of the degree of association/repulsion between a verb lemma in the progressive and a corpus (sample). It is calculated on the basis of a comparison between a verb lemma’s expected frequency in the progressive in a corpus (sample) if the null-hypothesis were true, i.e., under the assumption that the variables are independent of each other.

### 4.2.3.2 Comparative Distinctive Collexeme Analysis

This section describes the methodology for investigating the lemmas occurring as the main verb in FVPs most strongly associated with either progressive or non-progressive constructions within each corpus. To this end, separate progressive vs. non-progressive DCAs are conducted for each corpus (sample) and the CL values are then used to compare the results across the textbook and reference (sub)corpora for each register. In other words, a pair of progressive/non-progressive DCAs is compared for each of the two registers, conversation and fiction.

A known methodological problem in conducting DCAs is the operationalisation of the non-occurrence of the construction under study (see Bybee 2010: 98; and responses in Gries 2012: 488; Gries 2015a: 511); in other words, in this case, operationalising what constitutes a non-progressive construction. Intuitively, the best way forward is to identify “a level of resolution on which to count constructions that is close to the phenomenon in question” (Gries 2015a: 511). At the most basic level, this could be the number of words, or better verbs. However, this same problem was essentially already encountered in 4.2.2.7, where a more refined solution involving the automatic extraction of finite verb phrases (FVPs) using POS-tagging and dependency parsing information was presented. Thus, this method will also be used in the comparative DCAs, such that FVPs will serve as the count unit for all progressive and non-progressive constructions in an alternation paradigm.

Crucially, in order for the CL values of the two parallel DCAs of any one register to be comparable, it is necessary to ensure that the number of FVPs in the two corpora be equal. This is because the log-likelihood test which forms the basis of the CL calculation is highly sensitive to sample size. As a result, the data collecting and pre-
processing procedure for the DCAs is more complex than for the CovCAs. The
following section describes the procedure developed to arrive at approximately the
same number of FVPs per corpus (sample) for each pair of DCAs.

The total FVP verb lemma counts were automatically computed across the entire corpora using POS and dependency information inferred by the Spacy model (following the same procedure as in the calculation of the F-coefficients of progressives, see 4.2.2.7). The counts of the progressive FVPs, on the other hand, were the result of the manual annotation procedure (see 4.2.2.3), which, in the case of the reference corpora (i.e., in the Spoken BNC2014 and the Youth Fiction corpus), was only carried out for a random subset of all the progressive CQL query results. Consequently, it was necessary to calculate suitable ratios by which to divide the total lemma frequency counts across the full reference corpora in an attempt to match the sample size of their respective TEC subcorpus. Evidently, taking a simple word or token ratio bears the risk of strongly distorting the CL values – not least because the Spoken BNC2014 features proportionally more verbs than Textbook Conversation. However, if we assume that FVPs are evenly distributed across the reference corpora (which visual inspection the corpus dispersion plots from the Sketch Engine interface suggests is a valid assumption), we may divide the total number of FVPs in Textbook Conversation (64,292) by the number of FVPs in the Spoken BNC2014 (1,751,040) to obtain an operational sample size ratio (0.0367). This ratio can then be used to calculate the number of random concordance lines retrieved using the CQL query which need to be manually sorted and annotated and ultimately included in the DCA in order to match the number of FVPs from Textbook Conversation. In other words, out of the 126,395 hits returned by the progressive CQL query for the entire Spoken BNC2014, a random sample of 4,641 (= 126,395*0.0367) was manually sorted and annotated. Post-manual filtering (see 4.2.2.3), this random sample yielded 3,444 progressives, which is the figure listed in the corresponding cell of the example contingency table for the Spoken BNC2014 sample (see Table 13).

Consequently, it was possible to draw up lists of all the lemmas found in the manually
annotated progressive concordance lines from both a subcorpus of the TEC and its
corresponding reference corpus. With the computational tools described above,
however, automatically identifying FVPs of phrasal and prepositional verbs with their
particles was not technically feasible. Consequently, unlike in the CovCAs described
in 4.2.3.1, the DCAs do not differentiate between verbs such as LOOK AFTER, LOOK
UP and LOOK FORWARD TO. Thus, the first data preparation step consisted in reducing
the verb lemmas in the annotated progressive concordance lines to their main verbs,
i.e., dropping the prepositions and particles of multi-word verbs. Two lists of these
single-word verb types, one for each register under study, were then compiled.
Subsequently, each list was used to count the number of FVPs with these lemmas as their main verbs in the corresponding TEC subcorpus and its full reference corpora. Since the FVPs had already been automatically extracted previously (see 4.2.2.7), this could easily be realised with a for-loop script. The resulting count tables consist of a row per verb lemma from that register's lemma list, a column for the number of FVPs in the TEC subcorpus of that register, and a column for the number of FVPs in the corresponding reference corpus (see Table 11 for an extract of such a table).

<table><caption>Table 11: Last three entries of the count table for the conversation register before normalisation</caption><thead><tr><td>Lemma</td><td>FVPs in Textbook Conversation</td><td>FVPs in the Spoken BNC2014</td></tr></thead><tbody><tr><td>WORRY</td><td>142</td><td>1,043</td></tr><tr><td>WRITE</td><td>182</td><td>2,840</td></tr><tr><td>YAWN</td><td>0</td><td>15</td></tr></tbody></table>

Next, this dataset with the total number of FVPs per lemma in each corpus was combined with the count lemma information gathered in the annotated progressive concordance tables (see 4.2.2). For the TEC subcorpora, this information theoretically suffices to compute individual contingency tables for each verb, with counts for both progressive and non-progressive verb lemmas as main verbs in FVPs, from which the CL of each lemma can be calculated. Table 12 presents an example contingency table for SAY in the Textbook Conversation subcorpus. The values in bold have been directly counted in the corpora. The others are inferred.

<table><caption>Table 12: Contingency table for SAY in Textbook Conversation (the figures in bold were directly counted from the subcorpus)</caption><thead><tr><th></th><th>SAY as the main verb<br>of an FVP</th><th>FVPs with verbs<br>other than SAY</th><th>Row totals</th></tr></thead><tbody><tr><td>Progressive</td><td>29</td><td>2,394</td><td>2,423</td></tr><tr><td>Non-progressive</td><td>788</td><td>53,796</td><td>54,584</td></tr><tr><td>Column totals</td><td>817</td><td>56,190</td><td>57,007</td></tr></tbody></table>

In the case of the TEC subcorpora, all progressives FVPs were manually filtered from the complete progressive CQL query results (see 4.2.1), but the FVPs were extracted and analysed for their main verb lemma automatically. These different methods led to four lemmas (LOOP, NOURISH, OVERCHANGE and UPSET) that had been manually annotated as main verbs of FVPs in the progressive concordance lines, not being subsequently identified as verbs by the Spacy model and thus not counted as lemmas in FVPs. Therefore, when subtracting the number of progressives from the total number of FVPs for these four lemmas, the non-progressive count returned a negative value. Since this is clearly non-sensical, these counts were adjusted to one, to reflect the fact that one progressive occurrence of each was in fact observed in this subcorpus.
This adjustment resulted in an increase of the total number of FVPs in Textbook Conversation to 57,007, as displayed in Table 12.

Note that, contrary to the grand total number of FVPs used to calculate the ratios described above, the FVPs row + column totals listed in the contingency Tables 12 and 13 reflect the number of FVPs with lemmas from the lemma lists compiled for each register (which include all the lemma types found in the progressive concordance lines of a textbook subcorpus and its corresponding reference corpus sample). This explains why the total of 64,292 FVPs in Textbook Conversation was first used to calculate a sampling ratio, but we now only see a total of 57,007 FVPs in Table 12. In the following, this total is referred to as the total number of ‘list-FVPs’.

As mentioned at the beginning of this section, in order to be able to compare two DCAs, the grand totals of all the contingency tables compared ought to be the equal. To achieve this, the number of list-FVPs from each textbook subcorpus was divided by the number of list-FVPs from its corresponding reference corpus sample. For conversation, this resulted in the following ratio: 57,007/1,526,439 = 0.0373. In other words, using the Spacy model, a total of 50,045 FVPs with SAY as a main verb were counted across the entire Spoken BNC2014, but applying the ratio above reduced this total to 1,869.001, rounded off to 1,869 in Table 13.

<table><caption>Table 13: Contingency table for SAY in the Spoken BNC2014 sample (the figures in bold have been directly counted, underlined approximated from the corpus)</caption><thead><tr><th></th><th>SAY as the main verb<br>of a FVPs</th><th>Total verb lemmas of<br>FVPs except SAY</th><th>Row totals</th></tr></thead><tbody><tr><td><em>Progressive</em></td><td><strong>214</strong></td><td>3,230</td><td><u>3,444</u></td></tr><tr><td><em>Non-progressive</em></td><td>1,655</td><td>51,908</td><td>53,563</td></tr><tr><td><em>Column totals</em></td><td><u>1869</u></td><td>55,138</td><td><strong>57,007</strong></td></tr></tbody></table>

However, similarly to the TEC subcorpora, these contingency tables for the reference corpora also included a number of lemmas for which the total number of non-progressive forms was negative. In some cases, this is presumably for the same reason as for the TEC subcorpora, i.e., that the Spacy model did not recognise certain lemmas as verbs. Looking at the list of the 131 lemmas with negative non-progressive counts in the Spoken BNC2014, this would appear to be the case for a number of lemmas whose noun form is typically more frequent than its verb form, e.g., CONTACT, PARTY, PROTEST, QUEUE, RACE and SWEAT, and which were, as a result, presumably disproportionately rarely tagged as verbs by the Spacy model.

In the majority of cases, however, these negative values are largely due to low-frequency lemmas which were observed in the sample of the 3,444 progressive concordance lines from the Spoken BNC2014 and thus included in the conversation
lemma list but, because they are very low frequency, whose post-normalisation FVP counts (i.e., after having applied the list-FVP ratio described above) were below one. The problem is demonstrated with the lemma ACTION which was observed once in a progressive construction among the sample of 3,444 progressives from the Spoken BNC2014 in the context of telling an anecdote about a complaint at work (40).

(40) cos he should do that and then just sent he should have sent a message through to either Jill yeah or John the new guy yeah and said I'm not actioning these just to let you know <BNC2014: SVD6>

According to the formula applied to track FVPs using the Spacy model (see 4.2.2.7), the extract printed above (40) is the only occurrence of the verb lemma ACTION across the entire Spoken BNC2014. Thus, the total number of FVPs with this ACTION as its main verb lemma for the full corpus is equal to one but, as a result of sampling ratio, only 0.0373 for the sample. Consequently, a negative non-progressive count is returned when subtracting the number of progressives from the total number of FVPs (see Table 14 for further examples of such lemmas). It goes without saying that for some of these lemmas, a combination of the first and second causes may well have contributed to negative non-progressive scores (e.g., infrequent word forms which, when they do occur, are unlikely to be verbs and thus not recognised as such by the Spacy model, e.g., ASPHALT or REV, both from the Spoken BNC2014 list).

<table><caption>Table 14: Examples of lemmas whose non-progressive FVP counts were negative in the Spoken BNC2014</caption><thead><tr><th></th><th>Total FVPs<br>(full corpus)</th><th>FVPs<br>(in sample)</th><th>Progressive FVPs<br>(in sample)</th><th>Non-progressives<br>FVPs (in sample)</th></tr></thead><tbody><tr><td>ACTION</td><td>1</td><td>0.04</td><td>1</td><td>-0.96</td></tr><tr><td>PARTY</td><td>15</td><td>0.56</td><td>1</td><td>-0.44</td></tr><tr><td>SHINE</td><td>19</td><td>0.71</td><td>1</td><td>-0.29</td></tr><tr><td>SWEAT</td><td>92</td><td>3.44</td><td>5</td><td>-1.56</td></tr></tbody></table>

In order to resolve this issue, removing all lemmas whose reference corpus non-progressive counts were negative and whose progressive counts were equal to just one (e.g., for the Spoken BNC2014, lemmas such as those displayed in Table 14) was considered. This makes sense because the raw frequencies are, in any case, too low to reach statistical significance when conducting a DCA. However, eliminating all of the lemmas corresponding to these criteria would risk overlooking lemmas which are proportionally over-represented in the corresponding TEC subcorpora. These include, for instance, the lemma SHINE, which was observed ten times in Textbook Conversation and which is therefore a potentially interesting candidate for the comparison of the DCAs in the conversation register. Consequently, only lemmas which fulfilled the criteria above and whose total FVP counts in the corresponding
TEC subcorpus was equal to zero were excluded from the count tables to be used for the DCAs. For the conversation DCAs, this resulted in the elimination of 97 unique verb lemmas (out of the original 589). For the few remaining lemmas whose FVP counts were still negative (e.g., SHINE), the number of FVPs was adjusted to match that of the number of progressives with that same lemma. Table 15 shows the data displayed in Table 14 once these adjustments had been made.

<table><caption>Table 15: Example lemma frequency counts for the Spoken BNC2014 after having adjusted for sampling/tagging errors</caption><thead><tr><th></th><th>Total FVPs<br>(full corpus)</th><th>FVPs<br>(in sample)</th><th>Progressive FVPs<br>(in sample)</th><th>Non-progressives<br>FVPs (in sample)</th></tr></thead><tbody><tr><th>PARTY</th><td>15</td><td>1</td><td>1</td><td>0</td></tr><tr><th>SHINE</th><td>19</td><td>1</td><td>1</td><td>0</td></tr><tr><th>SWEAT</th><td>92</td><td>5</td><td>5</td><td>0</td></tr></tbody></table>

Following the data wrangling described above, the total number of list-FVPs included in the conversation DCAs corresponded to 57,006.63 (see Table 16).

<table><caption>Table 16: Contingency table for SAY in the Spoken BNC2014 post-adjustments</caption><thead><tr><th></th><th>SAY as the main verb of<br>a FVPs</th><th>FVPs with main verb<br>other than SAY</th><th>Row totals</th></tr></thead><tbody><tr><th>Progressive</th><td><strong>214</strong></td><td>3,133</td><td><strong>3,447</strong></td></tr><tr><th>Non-progressive</th><td>1655.00</td><td>52,004.63</td><td>53,659.63</td></tr><tr><th>Column totals</th><td><u>1869.00</u></td><td>55,137.63</td><td><strong>57,006.63</strong></td></tr></tbody></table>

There remains a difference of 0.37 more FVPs in Table 12 (Textbook Conversation) than in Table 16 for the corresponding Spoken BNC2014 reference sample (a difference corresponding to 0.0006%). Fortunately, Gries's (2019b) {coll_analysis} script allows the user to manually enter row totals for the contingency tables that diverge from the actual totals in the datasets. It is therefore possible to adjust the row totals of the contingency tables fed into the formula so that, for the purposes of the CL values computation, the total number of FVPs are matched exactly. Thus, 57,007 was entered as the total number of FVPs for the Spoken BNC2014 analysis. The same procedure was followed for the comparison of the progressive vs. non-progressive DCAs computed from the Textbook Fiction and Youth Fiction data.

A final issue concerns the choice of association measure for the DCAs. Fisher's Exact test (FET) can only be performed with integer values. Therefore, all DCAs are conducted using the log-likelihood ratio (LLR, $G^2$) to calculate the collostructional strength (CL) values. The LLR is calculated as shown in (41), where expected values are computed from row and column totals of the contingency table (Gries 2019a: 387).

$$ (41) \qquad 2 \sum_a^d \text{obs} \log \frac{\text{obs}}{\text{exp}} $$
LLR is currently the second most widely used association measure in collostructional analysis (Gries 2019a: 386). Like FET, it does not rely on or make any distributional assumptions, nor does it make specific sample size demands. In fact, the two measures are highly correlated and Evert (2009: 1235) demonstrated that “log-likelihood provides an excellent approximation to association scores computed by Fisher’s test”.

Controlling for the total number of FVPs allowed for a better comparison of the results of the parallel DCAs because the log-likelihood ratio ($G^2$) association measure which was chosen as the basis of the CL calculation is highly sensitive to sample size (like other association measures, see e.g., Gries 2019a: 388–390). The results of the DCAs were first analysed individually. Then, the CL values were standardised and visually compared with scatterplots.

All quantitative analyses were supplemented with extensive close reading of the relevant concordance lines, examples of which are given throughout the following results sections.

### 4.2.4 Correspondence Analysis

Following Deshors (2017), Correspondence Analysis (CA) was also used to explore the relationships between three of the dimensions under study, namely ‘Semantic domain’, ‘Variety’ (Textbook English vs. ENL) and ‘Register’ (Conversation vs. Fiction). CA is an extension of Principal Component Analysis and is used for exploratory analysis of interactions among categorical variables. It enables us to simplify, summarise and visualise complex contingency tables on two-dimensional plots (Desagulier 2017: 257–269; Husson, Lê & Pagès 2017: Ch. 2). Since the present study only deals with two different registers and two different varieties, a binary CA with the variables ‘Semantic domain’ and ‘Corpus’ suffices to effectively summarise the data (unlike in Deshors’ [2017] study where a Multiple Correspondence Analysis was necessary).

As a bottom-up approach, CA makes no prior assumptions concerning the groupings to be unveiled in the data; however, the variables must be globally interdependent for the method to be valid. To this end, a chi-square test of independence of deviation between the two variables must be conducted before proceeding (Desagulier 2017: 261). The CA itself outputs factor scores for both the row points and column points of the contingency table (corresponding here to a ‘Semantic domain’ by ‘Corpus’ cross-tabulation). The scores computed then function as the coordinates for the biplot which helps to visualise potential associations between the variables, whereby “two rows or two columns have a similar profile if they are relatively close in the Euclidean space” (Desagulier 2017: 266).
The intensity of the relationship is measured with Cramér’s V (Husson, Lê & Pagès 2017: 82) where a score of 0 would indicate no association, whereas 1 would point to the (highly unlikely!) exclusive association of semantic domains of progressives with the variable ‘Corpus’. In CA, Cramér’s V reflects the degree of variance within the contingency table (Desagulier 2017: 263).

The correspondence analysis was conducted with the default settings of the CA function of the R package {FactoMineR} (Lê, Josse & Husson 2008).

## 4.3 Results and discussion

### 4.3.1 Frequency of progressives

For the Textbook Conversation subcorpus the relative frequency of progressives, as approximated using the F-coefficient method described in 4.2.2.7, is 395.48 progressives per 10,000 finite verb phrases. By contrast, it was found to be 557.98 per 10,000 finite verb phrases in the Spoken BNC2014 sample. Thus, even when accounting for the lower number of verbs and finite verb phrases in Textbook Conversation (see 4.2.2.7), the F-coefficient of progressives is significantly lower in Textbook Conversation than in the reference ENL conversation corpus ($\chi^2(1) = 191.81, p < 0.001$). Though the difference appears impressive, the strength of the association is in fact very small ($\phi = 0.038$). Nonetheless, this finding contradicts the claim made by Biber & Reppen (2002b: 203) that the progressive aspect is overrepresented in textbook representations of natural conversation.

The Textbook Fiction subcorpus, on the other hand, has a slightly higher relative frequency of progressives than its corresponding reference corpus, the Youth Fiction corpus. However, this numerical difference is neither significant at the threshold of $p < 0.01$, nor meaningful in terms of its effect size ($\chi^2(1) = 5.7508, p = 0.015, \phi = 0.009$).

### 4.3.2 Morphosyntactic features

This section focuses on morphosyntactic features of the progressive constructions found in Textbook Conversation and Fiction. The features have been chosen on the basis of previous studies on the progressive aspect (in particular Römer 2005; Rautionaho 2014; and Deshors 2017). Raw figures are comparable across the two varieties (Textbook vs. naturally occurring ENL) because the following analyses are based on all the progressives retrieved from the Textbook Conversation and Textbook Fiction texts, as compared to the same number of random progressive concordance lines from the Spoken BNC2014 and the Youth Fiction corpus, 2,423 and 1,517 respectively.
4.3.2.1 Tense, aspect and modality

This first section focuses on the distributions of tense forms of the progressive which,
for the purposes of this analysis, also include the perfect aspect and modal forms.
Römer (2005: 244) reported that EFL textbook dialogues vastly overuses the present
progressive. The results displayed in Fig. 4 would, at first sight, appear to back this
finding for modern school EFL textbooks since the present progressive accounts for
75.65% of all progressives in Textbook Conversation, as opposed to 62.65% in the
Spoken BNC2014 sample. Furthermore, Römer (2005: 271) reports an under-
representation of present perfect and past perfect progressives with a number of high-
frequency verbs (e.g., ASK, GO, PLAY and STAY). Since the proportional usage of both
past and present perfect progressives is very low, the two forms were merged into one
'perfect' category. The present results (see Fig. 4) suggest that there is, indeed, an
under-representation of the past progressive in Textbook Conversation. By contrast,
perfect progressives seem, if anything, to be over-represented compared to the Spoken
BNC2014 sample.

The final category, entitled ‘modal’, contains progressives combined with CAN, COULD,
HAVE TO, MAY, MUST, SHOULD, WILL and WOULD. Though still very marginal, the
proportion of modal progressives relative to other progressive forms is almost twice
as high in the Spoken BNC2014 sample (3.76 %) as in Textbook Conversation (2.15
%). We might have expected modal progressives in Textbook Conversation to feature
a much more restrained range of modals; however, the full range is covered, thus with
very low frequencies of occurrence for each modal per textbook series. In contrast,
WILL and WOULD dominate in the Spoken BNC2014.

<figure><img src="image_7.png" /><figcaption>Fig. 4: Distribution of progressive forms in Textbook Conversation and the Spoken BNC2014</figcaption></figure>
The distributions are significantly different ($\chi^2(3) = 145.57, p < 0.001$)<fnref n="23" /> and whilst the strength of the association is small, it nevertheless suggests a meaningful association worth considering (Cramér's V = 0.173). Fig. 5 is an association plot (Meyer et al. 2003) which displays Pearson residuals for each tense form category as bars (a positive residual corresponds to an observed frequency being greater than expected). The intensity of the shading corresponds to its relative importance whereas the width of the bars represents the squared root of the expected value of each category. Thus, contrary to what Fig. 4 may have led us to believe, Fig. 5 shows that the most striking difference in the distribution of tense forms is in fact in the past progressive. Standardised Pearson residuals (as recommended by Agresti 2002: 81 and cited in Levshina 2015: 220-221) confirm that, whilst all categories make significant contributions to the $\chi^2$ statistic at the significance level of 0.01, with a standardised Pearson residual of 11.16, past progressive is the category with by far the greatest contribution.

<figure><img src="image_8.png" /><figcaption>Fig. 5: Association plot comparing the progressive form distributions in Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

However, it seems problematic to examine tense form distributions across the entire Textbook Conversation subcorpus when we know that textbook authors are unlikely to introduce more complex forms like past and perfect progressives until a later stage. Consequently, Fig. 6 displays the tense distributions subdivided by textbook level (level A corresponding to the first year of EFL learning in lower secondary school, see 3.3.1). As expected, the beginner textbooks almost exclusively feature present progressive forms whilst the perfect and modal progressives are not introduced until

<footnote n="23">²³ All $\chi^2$ tests conducted for this chapter were performed using the default settings of the chisq.test function in R. As such, Yates' continuity correction was applied for all 2 by 2 tables. A prerequisite for $\chi^2$ tests is that the observations are independent. Although now a standard test in qualitative corpus linguistics, it could be argued that this condition is not met since many concordance lines come from the same textbook (series) and some from the same text. The same is true of the reference corpora, though this issue is much more limited due to the random sampling of concordance lines.</footnote>
the third year of EFL instruction. Once they have been introduced, perfect progressives appear to be over-represented in Textbook Conversation. Modal progressives, on the other hand, are introduced more gradually. Whilst Fig. 6 shows that the present progressive is, in fact, not over-represented in Textbook Conversation, it is clear that past progressives, on the other hand, are under-represented across all textbook levels.

<figure><img src="image_9.png" /><figcaption>Fig. 6: Progressive form distributions in the Spoken BNC2014 sample and the five different Textbook levels<fnref n="24" /></figcaption></figure>

The same analyses as above were replicated for the Textbook Fiction and Youth Fiction progressive concordance lines. As shown in Fig. 7, fewer discrepancies in tense form distributions were found. The strength of association between the tense distribution and the corpora is even weaker than in the previous analysis (Cramér's *V* = 0.132). As in Textbook Conversation, the fact that the present progressive is seemingly over-represented in Textbook Conversation ensues from the late introduction of other progressive tense forms in the textbook series. Indeed, the distribution of progressive tense forms in level D and E textbooks corresponds well to that observed in the Youth Fiction. Modal progressives are not introduced until level D and, in the most advanced textbook of each series, the proportion of modal progressives in Textbook Fiction texts matches that of the reference fiction corpus.

<footnote n="24">²⁴ Note that the level E textbooks do not feature fewer progressives in their dialogues. The bar representing level E textbooks is narrower because the Textbook Corpus contains two series which only go as far as level D (*Solutions* and *Piece of Cake*).</footnote>
<figure><img src="image_10.png" /><figcaption>Fig. 7: Association plot comparing the tense form distributions in Textbook Fiction and the Youth Fiction sample</figcaption></figure>

### 4.3.2.2 Contractions

In naturally occurring spoken English, verb contractions are very frequent and, indeed, out of the 2,423 progressive concordance lines extracted at random from the Spoken BNC2014, over half (n = 1,285) feature a contracted form of the auxiliary BE. This rate has seemingly not changed over the past few decades: Römer (2005: 245) reported very similar proportions for her sample of progressives from the Spoken BNC1994 and the spoken section of the Bank of English.

Taken as a whole, the proportion of contracted forms in the Textbook Conversation subcorpus is only marginally lower (46.43%) than that of the reference corpus (53.03%). Though the difference is significant ($\chi^2(1) = 20.868, p < 0.001$), the strength of the association between the proportion of contracted forms and language variety (Textbook English vs. ENL) is negligible ($\phi^2 = 0.066$). However, a differentiated view of the proportions per Textbook series reveals a different picture (see Table 17). In the present study, the two lowest proportions of contracted forms come from French publications: *Join the Team* (29.41%) and *Piece of Cake* (39.26%).
<table><caption>Table 17: Proportion (and raw numbers) of contracted forms of the auxiliary BE in progressive constructions</caption><thead><tr><th></th><th>Contracted forms</th><th>Full forms</th></tr></thead><tbody><tr><td>Spoken BNC2014 sample</td><td>53.03% (1285)</td><td>49.97% (1138)</td></tr><tr><td>Access</td><td>54.07% (226)</td><td>45.93% (192)</td></tr><tr><td>Achievers</td><td>42.57% (43)</td><td>57.43% (58)</td></tr><tr><td>English in mind</td><td>57.02% (138)</td><td>42.98% (104)</td></tr><tr><td>Green line</td><td>43.17% (60)</td><td>56.83% (79)</td></tr><tr><td>Hi there</td><td>44.13% (94)</td><td>55.87% (119)</td></tr><tr><td>Join the team</td><td>29.41% (35)</td><td>70.59% (84)</td></tr><tr><td>New green line</td><td>44.50% (182)</td><td>55.50% (227)</td></tr><tr><td>Piece of cake</td><td>39.26% (64)</td><td>60.74% (99)</td></tr><tr><td>Solutions</td><td>45.72% (283)</td><td>54.28% (336)</td></tr></tbody></table>

Fig. 8 displays the proportions of contracted to full forms per progressive form. It shows that, in both Textbook Conversation and the Spoken BNC2014, contracted forms occur considerably more frequently in present progressives than in any other progressive form. More than three quarters of all present progressives in the BNC sample are contracted forms. This proportion is considerably lower in Textbook Conversation. It is also notable that, whilst perfect progressives are proportionally more frequent in Textbook Conversation, the vast majority are in non-contracted forms, whereas around half of naturally occurring perfect progressives feature a contraction. Past progressives, on the other hand, are very rarely contracted and this is well reflected in the textbook data.

<figure><img src="image_11.png" /><figcaption>Fig. 8: Proportion of contracted progressives by progressive form in Textbook Conversation as compared to the Spoken BNC2014 sample</figcaption></figure>
Compared to Conversation, narrative writing features considerably fewer contracted progressive forms. No significant difference in the proportion of contracted progressives between the Textbook Fiction subcorpus and the Youth Fiction reference corpus ($\chi^2(1) = 0.097478, p = 0.75$) was observed (see Table 18).

<table><caption>Table 18: Proportions of contracted and non-contracted progressives in Textbook Fiction and the Youth Fiction corpus</caption><thead><tr><th></th><th>Contracted form</th><th>Full form</th></tr></thead><tbody><tr><td>Youth Fiction sampled</td><td>21.23% (322)</td><td>78.77% (1195)</td></tr><tr><td>Textbook Fiction</td><td>20.70% (314)</td><td>79.30% (1203)</td></tr></tbody></table>

As discussed in 3.3.1.4, a few textbook series hardly feature any narrative texts (e.g., *Piece of Cake* and *Solutions*). Disregarding those, the series with the lowest proportion of contracted progressives in Textbook Fiction remains, as in Textbook Conversation, the French series *Join the Team* (13.33%). *Achievers* also has a low rate (15.26%). The highest rates are found in *Hi There* (32.73%) and *Access* (26.76%).

The distributions of contracted auxiliaries across tense forms and subject person forms (*I*, *you*, *he/she/it*, etc.) in the two TEC subcorpora are very similar to that of the reference corpora.

### 4.3.2.3 Negation

Though the proportion of negated progressives observed in Textbook Conversation is marginally lower than in the Spoken BNC2014 sample (see Table 19), no meaningful quantitative difference could be established within the present datasets ($\chi^2(1) = 4.1278, p < 0.001, \phi = 0.03$). This was also true of the Textbook Fiction vs. Youth Fiction comparison (see Table 20; $\chi^2(1) = 8.8826, p = 0.003, \phi = 0.06$). Nor were any significant differences between the different Textbook series found.

<table><caption>Table 19: Proportion of negated progressives in Textbook Conversation and the Spoken BNC2014 sample</caption><thead><tr><th></th><th>Negated forms</th><th>Non-negated forms</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>6.48% (157)</td><td>93.52% (2266)</td></tr><tr><td>Spoken BNC2014 sample</td><td>5.08% (123)</td><td>94.92% (2300)</td></tr></tbody></table>

<table><caption>Table 20: Proportion of negated progressives in Textbook Fiction texts and the Youth Fiction sample</caption><thead><tr><th></th><th>Negated forms</th><th>Non-negated forms</th></tr></thead><tbody><tr><td>Textbook Fiction</td><td>3.10% (47)</td><td>96.90% (1470)</td></tr><tr><td>Youth Fiction sample</td><td>5.34% (81)</td><td>94.66% (1436)</td></tr></tbody></table>
However, qualitatively, it would appear that negation occurs in quite different functional and semantic contexts. Thus, in Textbook Conversation, negated progressives are typically found where an action is not taking place (42) or a state is negated (43). By contrast, natural conversations feature more misunderstandings which call for clarifications, and these frequently rely on negated verbs, e.g., (44)–(46).

(42) What's he doing? I don't know he **isn't answering** his phone.
<TEC: Solutions Pre-intermediate>

(43) Sue, you **aren't wearing** your sports kit. P.E. starts in ten minutes.
<TEC: Solutions Intermediate>

(44) I **am not talking** about finding a job <BNC2014: S2FQ>

(45) hold on **we're not using** that one <BNC2014: S632>

(46) it's not crazy logic and I'm **not being** funny in the odd way
<BNC2014: SG2Y>

Moreover, the topics dealt with in school EFL textbooks tend to avoid controversy and the vast majority of dialogues thus depict positive situations and conversations between people who generally agree with each other. This observation echoes the conclusions of earlier textbook studies (e.g., Rinvolucri 1999; Wajnryb 1996). Indeed, to Wajnryb (1996, cited in Timmis 2016: 5), the world depicted in textbooks is “safe, clean, harmonious, benevolent, undisturbed, and PG-rated”. By contrast, perfectly harmonious interactions are clearly not always to be found in authentic conversational data and this is likely another factor that contributes to substantial qualitative differences in the type of negation found in the Spoken BNC2014 (47)–(49) as opposed to Textbook Conversation.

(47) yeah yes **we're not looking** forward to that <BNC2014: SV4W>

(48) I'm **not trying** to be very mean but she sort [sic] of people that just
wanna do it by themselves like their own way and don't listen
<BNC2014: S7WY>

(49) you **are not buying** dodgy tablets off ebay or anything? <BNC2014: SPZR>

### 4.3.2.4 Questions

Römer (2005: 252) observed that almost twice as many progressives in interrogative contexts are found in textbook dialogues than in naturally occurring speech (20.48% versus 10.77%). This trend is confirmed in the present data set, with 17.91% of all progressives in the Textbook Conversation subcorpus being in the form of questions, compared to 11.39% in the Spoken BNC2014 sample. The difference in proportion is statistically significant ($\chi^2(1) = 40.379, p < 0.001$), but the strength of the association is very small ($\phi^2 = 0.092$). Once again, the most notable differences are qualitative in nature. Textbooks feature many more closed questions in the progressive (50) and
questions usually query what is happening right now, even in cases where the context
of a shared environment probably ought to render this unnecessary (51).

(50) **Are you swimming in the sea? Are you playing golf in the jungle?**
<TEC: Achievers Pre-intermediate>

(51) Jenny: What **are you doing**?
Terry: I'm watching TV.
Jenny: Why **are you watching** TV? You usually play football on Saturdays.
Terry: But it's raining. <TEC: Piece of cake 6>e>

By contrast, questions in the progressive in the Spoken BNC2014 are frequently
rhetorical (52) or serve to ask for confirmation (53)-(1) and often do not follow the
"standard" question syntax typically taught in school textbooks, e.g., (54) and (1).

(52) okay get it recorded I'm **guessing**? yeah excellent no one is actually listening to this picking out words okay? <BNC2014: SG87>

(53) **isn't she having** another baby? <BNC2014: SJDM>

(54) **you're not buying** dodgy tablets off ebay or anything? <BNC2014: SPZR>

(1) there you are cos er <anon type="name" nameType="m"/> **wasn't using** it then?
<BNC2014: S263>

These notable differences in the use of both negated and question-form progressives
in textbook conversation as compared to naturally occurring interactions between
native speakers may be associated with Carter's (1998: 47) claim that many
coursebook dialogues portray:

a ‘can do’ society, in which interaction is generally smooth and problem-free, the speakers co-operate with each other politely, [...] and the questions and answers are sequenced rather in the manner of a quiz show or court-room interrogation.

Similarly, in his comparison of textbook vs. authentic service encounter dialogues, Gilmore (2004; see 2.2.4) also observed that, unlike textbook conversations, naturally occurring dialogues contain a lot of repetition, backtracking, misunderstandings and clarifications. Indeed, excerpts such as (51) seem to suggest that, at least in the use of questions in textbook dialogues, little has changed over the past decades.

Progressives in interrogative contexts are less frequent in both the Textbook Fiction subcorpus and the Youth Fiction reference corpus (7.25% and 8.44% of progressive concordance lines, respectively). No significant difference between the two corpora was found concerning this variable ($\chi^2(1) = 1.3055, p = 0.25$).

4.3.2.5 Voice

Römer (2005: 3) did not include the active-passive paradigm in her study on the progressive in contemporary ENL and Textbook English; therefore, to the present author's best knowledge, this is the first investigation of progressive passives in EFL textbook language.
Overall, Table 21 shows that passive progressives are very rare across all four sub(corpora) under study. Whilst the raw figures may imply that progressive passives are slightly underrepresented in the TEC subcorpora, statistical tests suggest that there is no meaningful association between the corpora and the proportions of active/passive voice progressives.

<table><caption>Table 21: Voice of progressive constructions</caption><thead><tr><th></th><th>Active</th><th>Passive</th><th>Unclear</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>99.42% (2409)</td><td>0.58% (14)</td><td>0.00% (0)</td></tr><tr><td>Spoken BNC2014 sample</td><td>99.01% (2399)</td><td>0.95% (23)</td><td>0.04% (1)</td></tr><tr><td>Textbook Fiction</td><td>99.41% (1508)</td><td>0.59% (9)</td><td>0.00% (0)</td></tr><tr><td>Youth Fiction sample</td><td>98.22% (1490)</td><td>1.78% (27)</td><td>0.00% (0)</td></tr></tbody></table>

Based on his comparison of LOB and FLOB, Smith (2005: Chapter 6) reports that progressive passives in the present tense have seen a dramatic increase in contemporary British English. He points out that this increase is all the more striking because, over the same period, a significant decrease in non-progressive passives has also been observed (Smith 2005: 138). However, Smith's analysis refers to published writing genres only. Due to the very low raw figures in the LOB and FLOB, he also compares the frequencies of progressive passives across the different genres of the BNC1994 and the ICE-GB and his comparison clearly shows that the lowest relative frequencies are found in conversation, with 29 and 113 progressive passives per million words (pmw) in the Spoken BNC1994 and the ICE-GB conversation subcorpora respectively, compared to over 400 pmw in newspaper writing, broadcast news and institutional documents.

Overall, Table 21 confirms these low frequencies of passive progressives for the conversation register. However, of these few occurrences, nearly a third ($n = 11$) were not in the present progressive, the tense for which the substantial increase in passive use has been observed, but rather in the past form (55)–(56).

(55) The man **was being chased** by a police officer because he'd stolen a gold necklace from a jewellery shop. <TEC: New Green Line 5>

(56) we just started talking about you and then we realised that we **were being recorded** <BNC2014: SP2X>

A qualitative analysis of the few passive progressives observed in either conversation corpora reveals a notable difference in the degree of colloquialism. Indeed, the Spoken BNC2014 sample features a total of six GET-passives (57) and one HAVE-passive in the progressive (58), whereas these are entirely absent from the Textbook Conversation subcorpus.

(57) how much **am I getting paid?** <BNC2014: SA7J>
(58) cos I know some women they're find it a bit difficult mm to cope with
the fact that **they're having their lady bits taken away** <BNC2014: S28F>

These alternative passive forms are generally considered more colloquial than
progressive passives formed with BE (Smith 2005: 125). Indeed, the BE-passives
featured in Textbook Conversation seem more typical of written genres than
spontaneous conversation (59)-(62).

(59) Countries that still use leaded petrol **are being urged** to stop using it
[...] <TEC: Solutions Intermediate Plus>

(60) Well, Amy, if you must know - It isn't only to watch sport. He's going to run in a triathlon. And **he's being sponsored**, you know, for charity.
<TEC: English in Mind 4>

(61) I push myself to the limit, and I'm usually covered in bruises! But I do
try to avoid injury, though it's difficult if you **are being taught** a new
move. <TEC: Solutions Intermediate>

(62) today in South Africa about one third of our water is being wasted
because of old pipes and insufficient infrastructure. <TEC: New
Mission 2<sup>de</sup>>

4.3.3 Functions of the progressive

4.3.3.1 Time reference

Fig. 9 shows considerable distributional differences in the time period progressives in Textbook Conversation and the Spoken BNC2014 sample refer to. The largest numerical differences are found in the categories ‘past’ and ‘present’ time reference but considering the distribution of tense forms reported in 4.3.2.1, this is to be expected. The high number of ‘unclear’ cases in the Spoken BNC2014 is likely due to the high degree of implicitness typical of spontaneous conversation between speakers who share a common environment and previous knowledge.
<figure><img src="image_12.png" /><figcaption>Fig. 9: Time period reference for the progressives in Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

The category ‘past/present’ time reference refers to events and situations which started in the past and are ongoing (63). Fig. 9 suggests that this time reference is over-represented in Textbook Conversation. Like the differences observed in the time reference categories ‘past’ and ‘present’, this is also due to the over-representation of present perfect forms in Textbook Conversation noted in 4.3.2.1.

(63) Come on! We’ve been waiting for fifteen minutes already!
<TEC: Green Line 4>

The differences observed in the time reference categories ‘future’, ‘general validity’, and ‘hypothetical’ correspond to differences previously reported by Römer (2005: 257) in her comparison of “school” and “real” English, though she groups the latter two time references in a single category: ‘present/future (“indeterminate”) time reference’. For this category, Römer reports a considerable difference in proportional usage (15.99% and 14.38% in spoken English versus 3.58% and 2.78% in school textbooks). At the same time, she also finds that future time reference is over-represented in EFL textbook dialogues (31.60% and 31.65% compared to 18.55% and 15.68% in her reference spoken English corpora).

Whilst interesting in its own right, Römer’s presentation of the results makes it difficult to tell which categories make a relevant contribution to the overall distributional difference. To remedy this in the context of the present study, an association plot (Meyer et al. 2003) of the data presented in Fig. 9 was produced (see Fig. 10). In Fig. 10, the colour of the shading shows that, whilst the overall time reference distributions are statistically different at *p* < 0.001 (Cramér’s V = 0.26), the category ‘future’ time reference does not make a statistically significant contribution to this distributional difference. By contrast, the proportions of
progressives found in the categories ‘general validity’ (64)–(65) and ‘hypothetical’ reference (66)–(67), are significantly associated with the corpora/variety so that Fig. 10 suggests a genuine under-representation of these functions in Textbook Conversation.

(64) her mum's not skinny either like her mum's a middle aged woman she's carrying a bit of weight around her stomach but she doesn't she's not fat but she's not skinny <BNC2014: SAMQ>

(65) and when she went for the entry exam the guy said right at this point you know those of you have passed need to do this and this to get ready for your fitness then he said you should be aiming to run a mile and a half in sixteen minutes well she's running it in fifteen and a half <BNC2014: SBYQ>

(66) no I think she would still be doing it
I think she probably would because I mean it would the same if I came
with everybody it would be even worse if you are all up there and I was
doing doing nothing it would be even worse if you are all up there and I
was doing nothing <BNC2014: S3M4>

(67) but when you're earning what I'm earning there's no way I'm paying off five hundred pound is there <BNC2014: SJLT>

<figure><img src="image_13.png" /><figcaption>Fig. 10: Association plot of the distribution of time references of progressives in Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

To understand the pedagogical decisions made by the textbook authors, it is important to take a differentiated look at the distribution of time references of progressives at each Textbook level (see Fig. 11). The proficiency levels of the textbooks of the TEC are labelled from A to E – A corresponding to first year secondary school textbooks (see Table 3 for details).
<figure><img src="image_14.png" /><figcaption>Fig. 11: Distribution of time references of progressives in the Spoken BNC2014 sample and in Textbook Conversation for each textbook level<fnref n="25" /></figcaption></figure>

What can be clearly seen in Fig. 11 is the strong dominance of the ‘present’ and ‘future’ time references in the beginner textbooks, which is to be expected since, in Fig. 4, these textbooks were shown to feature almost exclusively present tense form progressives. Moreover, it is evident that the present perfect form and its prototypical past/present time reference are not introduced until the third year of EFL teaching (here: level C).

More importantly, Fig. 11 reveals that the time reference distributions of the intermediate and advanced textbooks (levels C to E) are, in fact, relatively close to that observed in the Spoken BNC2014 sample. Whilst an independence χ² test of this subset of the data nevertheless showed a significant association between time reference distribution and naturally occurring/textbook conversation (χ²(5) = 88.371, p < 0.001), the very modest strength of association (Cramér’s V = 0.15) confirms the fact that the distribution of time references of the more advanced textbooks does indeed match relatively well that of naturally occurring conversation. What is more, the association plot between advanced Textbook Conversation – levels C to E only – and the Spoken BNC2014 sample (see Fig. 12) shows that the most important differences are found in the categories ‘unclear’, which is of little pedagogical

<footnote n="25">²⁵ Note that for ease of comparability, ‘unclear’ concordance lines were removed from this mosaic plot.</footnote>
relevance, and ‘past/present’. This latter divergence will be further explored in the
following section.

<figure><img src="image_15.png" /><figcaption>Fig. 12: Association plot of progressive time reference between Level C-E Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

The Textbook Fiction vs. Youth Fiction comparison revealed no notable differences
in the distributions of time reference of progressives. Whilst significant overall
($\chi^2(6) = 41.701, p < 0.001$, Cramér's $V = 0.12$), the only category that makes a
significant contribution to this difference is the ‘present’ time reference which, as
described above for Textbook Conversation, is due to the “weight” of the beginner
textbooks in the subcorpus. Indeed, an association plot comparing the progressives in
the narrative texts of the three more advanced textbooks in each textbook series with
those of the Youth Fiction corpus yielded no significant difference between the time
reference distributions ($\chi^2(5) = 11.662, p = 0.04$).

4.3.3.2 Time reference, tense and modality

Fig. 13 displays a cross tabulation of language variety (top and bottom plots),
progressive tense form (vertical subdivisions) and time reference (horizontal
subdivisions). It enables a more fine-grained comparison of the functional usage of
the different progressive tense forms across Textbook and naturally occurring ENL
conversation.
<figure><img src="image_16.png" /><figcaption>Fig. 13: Mosaic plot displaying the cross-tabulation of forms and time references of progressives in Textbook Conversation and the Spoken BNC2014 sample (progressives with ‘unclear’ time references were removed for this analysis).</figcaption></figure>

It shows that, in Textbook Conversation, perfect forms are used to refer to both events that started in the past and are still ongoing and past events which preceded another past event. This is due to the fact that a number of textbook series introduce the past perfect progressive form in the more advanced EFL textbooks (68)–(70) even though this form is very rare in naturally occurring conversation (no single occurrence among the 2,423 random progressive concordance lines from the Spoken BNC2014).

(68) I wonder if anyone **had been living** here before Verrazano came.
<TEC: Green Line 4>

(69) His name was Mike. He was one of the owners of the hostel and **had been taking care** of the terrace plants. <TEC: New Green Line 5>

(70) I got home I and did an Internet search. It didn't take long. I **had been searching** for less than a minute when I found the answer: the artist I was looking for was JR. <TEC: Access 5>

It may be noted that the three past perfect progressive examples from the Textbook Conversation sub-corpus (68)–(70) are all taken from German textbook series. Strikingly, a CQL search for past progressives across all textbook registers confirmed that this form is particularly frequent in German textbooks (accounting for 59.67% of all occurrences in the full Textbook English Corpus).

Fig. 13 also reveals that British English native speakers are more likely to resort to *modal + progressive* constructions (foremost WILL + BE + -ing) to refer to (possible) future events (71)–(73) than the interlocutors of the Textbook dialogues.

(71) it was a special little treat
I don't think I'll **be going** back again any time soon though
unfortunately
might do that for like graduation or something that'd be nice
<BNC2014: SEM7>

(72) even if it turns out to be shit at least it's a plan
yeah
and at least it means that I'll be working towards something so
<BNC2014: SYTD>

(73) Nunnington Hall **will be reopening** soon cos it
will it?
closes over winter <BNC2014: S4QF>

The few occurrences of past tense forms with future time reference from the Spoken BNC2014 are due to reported speech and thought (74).

(74) I've got to go to Waitrose to pick up the turkey when **are you working**
and stuff like that?
I thought **you were getting** turkey Thursday
no **getting** the turkey Wednesday after work
where you gonna put it? <BNC2014: SNK6>

In sum, though Fig. 13 shows fairly similar distributions of tense forms cross-
tabulated by time reference in Textbook Conversation and the Spoken BNC2014, a
few notable differences have been highlighted above. By contrast, the equivalent
analysis for Textbook Fiction and Youth Fiction did not point to any further
potentially relevant differences worth investigating in more depth. The only
substantially different tense form distribution is found in the time reference category
'past/present' which, as above for Textbook Conversation, is made up exclusively of
present perfect progressive forms in the TEC subcorpus, whereas Youth Fiction also
includes past progressives in this time reference category.

4.3.3.3 (Non-)continuousness

Many EFL grammars, and indeed the three textbook series representing the Spanish Textbook subcorpus (*Achievers, English in Mind* and *Solutions*), refer to the progressive construction as “the continuous”. It may therefore come as a surprise that a considerable proportion of progressives in the two reference corpora (21.38% in the Spoken BNC2014 sample and 15.53% in the Youth Fiction sample) actually refers to ‘non-continuous’ actions (75).

(75) so that's why I kind of like keep my answers short when she's when **she's**
**asking** me how I am mm <BNC2014: S954>

By contrast, in the two Textbook English registers under study, progressives are less
frequently used for ‘non-continuous’ actions (see Table 22). Table 22 shows that the
proportion of ‘non-continuous’ progressives in the Spoken BNC2014 is almost twice
as high as in Textbook Conversation. The null hypothesis that there is no association
between naturally occurring/textbook conversation and the proportion of ‘non-
continuous’ progressives can be rejected for both the conversation register
(χ²(1) = 131.04, p < 0.001, φ = 0.14) and the Textbook Fiction/Youth Fiction pair (χ²(1) = 26.76, p < 0.001, φ = 0.09).<fnref n="26" />

<table><caption>Table 22: The continuousness function of progressives in the two textbook registers and reference corpora</caption><thead><tr><th></th><th>Continuous</th><th>Non-continuous</th><th>Unclear</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>88.40% (2412)</td><td>11.35% (275)</td><td>0.25% (6)</td></tr><tr><td>Spoken BNC2014 sample</td><td>75.57% (1831)</td><td>21.38% (518)</td><td>3.05% (74)</td></tr><tr><td>Textbook Fiction</td><td>89.78% (1362)</td><td>9.16% (139)</td><td>1.05% (16)</td></tr><tr><td>Youth Fiction sample</td><td>83.78% (1271)</td><td>15.43% (234)</td><td>0.79% (12)</td></tr></tbody></table>

A lexico-grammatical approach to the continuous/non-continuous paradigm offers further insights into the differences between progressive usage in naturally occurring ENL conversation and Textbook Conversation. Table 23 lists the lemmas that occur most frequently with a ‘non-continuous’ function in either corpora. Some frequency ranks in the Textbook Conversation column contain more than one lemma whenever these have the same number of occurrences in a ‘non-continuous’ context. These raw numbers of occurrences (out of the total of 2,423 progressives investigated in each of the two datasets) is indicated in brackets. Additionally, the percentages in brackets refer to the proportions of ‘non-continuous’ usage for each lemma in the corresponding corpus.<fnref n="27" />

<footnote n="26">²⁶ Concordance lines for which it was not possible to reasonably guess whether a progressive was referring to a continuous or non-continuous action (i.e., ‘unclear’) were not taken into account for both these χ² tests.</footnote>

<footnote n="27">²⁷ Note that a number of lemmas absent from the Spoken BNC2014 rank list, such as ASK, BUY, LEAVE AND pick up, display similarly high proportional usage of the ‘non-continuous’ function in naturally occurring conversation, but are overall comparatively far less frequent in progressive constructions than in Textbook Conversation, hence why they do not appear in this rank table.</footnote>
<table><caption>Table 23: Most frequent lemmas occurring in the progressive with a 'non-continuous' function in Textbook Conversation and the Spoken BNC2014 (total occurrences in 'non-continuous' function) (as a percentage of all progressive occurrences)</caption><thead><tr><th>Rank</th><th>Spoken BNC2014 sample</th><th>Textbook Conversation</th></tr></thead><tbody><tr><td>1</td><td>SAY (77) (51%)</td><td>GO (52) (32%)</td></tr><tr><td>2</td><td>GO (76) (48%)</td><td>COME (16) (30%)</td></tr><tr><td>3</td><td>GET (36) (49%)</td><td>ASK (9) (60%) / LEAVE (60%) / SAY (31%) / START (64%)</td></tr><tr><td>4</td><td>COME (31) (60%)</td><td>BUY (8) (67%)</td></tr><tr><td>5</td><td>HAVE (21) (34%)</td><td>TAKE (7) (21%)</td></tr><tr><td>6</td><td>TAKE (17) (65%)</td><td>GET (6) (14%) / JOKE (55%) / MEET (46%) / TELL (32%)</td></tr><tr><td>7</td><td>TELL (14) (52%)</td><td>CALL (4) (33%) / DO (2%) / HAVE (7%) / KID (31%) / PICK UP (100%) / SEND (100%)</td></tr></tbody></table>

Table 23 indicates that, in the Spoken BNC2014 sample, SAY is the verb lemma most frequently associated with the non-continuous function in the progressive (see Table 23). In this sample, every other progressive occurrence of SAY refers to a 'non-continuous action' (76)–(77), whereas two thirds of progressive SAY occurrences in Textbook Conversation actually refer to 'continuous' actions, ongoing at the time of speaking, e.g., (79).

(76) but I read the text and the text is basically **saying** that there's three parts to people there's the emotional which is what you are when you are in your mother's womb <BNC2014: SEKZ>

(77) paid off the builders yeah well they're **saying** the the if they do expand the A14 it will be paid for the tolls <BNC2014: S8LS>

(78) deal with the estate agents deal with the plans like he **was** **saying** you've got to have a look at the whole planning act <BNC2014: SG56>

(79) A: Did they enjoy your talk?
B: I don't think so. I don't think they understand a word of what I said.
A: What's **he** **saying** now?
B: I don't know. It's too noisy in here. <TEC: English in Mind 4>

A number of other verb lemma, such as ASK, GET, HAVE, TAKE and TELL, follow a similar trend: in the naturally occurring conversation data, progressive forms frequently express non-continuousness (80)–(81), whilst in the textbook dialogues, the focus is still refer to something procedural (82) or to ongoing actions (83).

(80) the last one I saw was erm having to pay tax on tampons oh I haven't seen that one it's it's **asking** the government to get rid of tax on tampons cos it or it's already tax free on crocodile meat <BNC2014: S6MQ>

(81) if you don't do this it's me I'll be **having** a go at you because it's my job to stop you getting diabetes <BNC2014: SGMT>
(82) So, let's get started! I'm sure you've been asking yourselves: What makes Seattle so special? <TEC: New Green Line 4>

(83) It's twelve o'clock at night in Edinburgh and we're having a great time, it's incredibly noisy here! <TEC: Achievers A>

#### 4.3.3.4 Repeatedness

Table 24 summarises the results of the repeatedness function analysis. It shows that progressives which express repeated actions or states are under-represented in Textbook Conversation ($\chi^2(1) = 30.52$, $p < 0.001$, $\phi = 0.082)^{28}$). In contrast, the proportion of progressives which refer to repeated and non-repeated actions in Textbook Fiction is not significantly different to that of the Youth Fiction sample ($\chi^2(1) = 12.61$, $p = 0.21$).

<table><caption>Table 24: The repeatedness function of progressives in the two textbook registers and reference corpora as a percentage of all progressives (and in absolute figures)</caption><thead><tr><th></th><th>Non-repeated</th><th>Repeated</th><th>Unclear</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>82.58% (2,001)</td><td>16.26% (394)</td><td>1.16% (28)</td></tr><tr><td>Spoken BNC2014 sample</td><td>69.29% (1,679)</td><td>20.68% (501)</td><td>10.03% (243)</td></tr><tr><td>Textbook Fiction</td><td>89.58% (1,359)</td><td>8.77% (133)</td><td>1.65% (25)</td></tr><tr><td>Youth Fiction sample</td><td>86.49% (1,312)</td><td>9.95% (151)</td><td>3.56% (54)</td></tr></tbody></table>

In the Spoken BNC2014 sample, a number of verb lemmas in the progressive very frequently express repeatedness. Among the top 30 verb lemmas most frequently observed in the progressive, these include PAY (84%), USE (60%), WORK (51%), WATCH (40%) and PUT (40%). The highest proportional use of the repeated function in Textbook Conversation is found with the lemma USE but, with 38%, at an appreciably lower rate than in the Spoken BNC. The second highest is WORK, with 36%.

Although the figures may not sound particularly alarming, as previously warned by Römer (2005: 284), little exposure to this core function could lead to genuine misunderstandings in the foreign language. For instance, EFL learners may not recognise the repeated nature of these events in the following extracts from the Spoken BNC2014, e.g., (84)–(85).

(84) oh they've taken the one that you put er compostables in and garden rubbish so that means people **are putting** them in the black bins <BNC2014: SRWD>

(85) **she was trying** to multitask everything that was going on in her life mm **she's she was** like getting chest pains and stuff from it <BNC2014: ST47>

<footnote n="28">28 This test of independence and the next were performed without the concordance lines labelled ‘unclear’ for these features, though, for repeatedness, it is worth noting that these make up a non-negligible 10% of the Spoken BNC2014 data.</footnote>
### 4.3.3.5 Additional functions of the progressives

Table 25 summarises the number of progressives coded with additional functions following an adaptation of Römer's (2005) functional categories (described in 4.2.2.5). Of those, the null hypothesis that there is no association between the prevalence of any one of these additional functions and the source of the data (i.e., Textbook vs. ENL reference data) can be rejected for both registers in the case of 'framing' (Conversation: $\chi^2(1) = 19.56, p < 0.001, \phi = 0.24$; Fiction: $\chi^2(1) = 26.13, p < 0.001, \phi = 0.25$) and 'gradual change/development' (Conversation: $\chi^2(1) = 9.55, p < 0.005, \phi = 0.17$; Fiction: $\chi^2(1) = 13.84, p < 0.001, \phi = 0.18$). No significant differences are observed for the other two categories.

<table><caption>Table 25: Percentages of progressives within each (sub)corpus coded for additional functions (raw figures are given in brackets)</caption><thead><tr><th></th><th>Emphasis<br>/shock</th><th>Framing</th><th>Gradual change/<br>development</th><th>Politeness/<br>softening</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>1.53% (37)</td><td>4.04% (98)</td><td>1.94% (47)</td><td>0.21% (5)</td></tr><tr><td>Spoken BNC2014</td><td>1.65% (40)</td><td>1.86% (45)</td><td>2.72% (66)</td><td>0.33% (8)</td></tr><tr><td>Textbook Fiction</td><td>0.79% (12)</td><td>9.49% (144)</td><td>3.36% (51)</td><td>0.13% (2)</td></tr><tr><td>Youth Fiction</td><td>1.32% (20)</td><td>5.21% (79)</td><td>3.36% (82)</td><td>0.13% (2)</td></tr></tbody></table>

The considerable over-representation of progressives in a 'framing' context in textbook registers undoubtedly stems from the fact that the 'framing' function is central to description of the progressive in the textbooks' grammar sections (see Römer 2005: 241). It is given particular emphasis when the past progressive is introduced, as a way to contrast the past progressive and the past simple. The analysis of the progressive concordance lines shows that this 'framing' use of the past progressive is modelled in many of the textbook dialogues and narrative texts (86)–(89). In contrast, the 'framing' function is less frequently featured in ENL fiction and is very rare in naturally occurring conversation (see Table 25).

(86) While he **was walking** through customs his clothes fell out of his bag. <TEC: Green Line 3>

(87) While they **were listening**, he thought about his parents. <TEC: New Green Line 4>

(88) Sorry I missed your call yesterday. I **was having** my dance class when you rang. <TEC: Hi There 5>

(89) While the Prince and his men **were riding** through the forest, they heard loud barking [...] <TEC: English in Mind 3>
4.3.4 Lexical and semantic aspects

4.3.4.1 Co-varying Collexeme Analyses of ‘Verb lemma’ + ‘Variety’

In order not to conflate the effects of language variety (i.e., Textbook English vs.
naturally occurring ENL) with register, two separate CovCAs comparing how each
verb lemma is attracted to or repelled by the progressive construction in each variety,
one for each register under study, were conducted. Accordingly, Table 26 presents a
ranking of the top twenty verb lemmas whose use in the progressive construction is
most strongly associated with either Textbook Conversation or the Spoken BNC2014,
whilst Table 27 lists the equivalent results for Textbook Fiction and the Youth
Fiction corpus. Both tables indicate the raw and expected frequencies of the top-
ranking verb lemmas in each corpus and the verb’s collostructional strength (CL),
which, here, represent the degree of association between a verb lemma and a variety.

The top-ranking collexemes associated with the progressive constructions in the two
conversation samples are displayed in Table 26. The table features some remarkably
high CL values, thus pointing to major differences in the lemmas featured in the
progressive in Textbook Conversation as compared to the Spoken BNC2014.
<table><caption>Table 26: Top 20 Co-varying Collexemes for ‘Verb’ + ‘Variety’ (Spoken BNC2014 vs. Textbook Conversation)</caption><thead><tr><th colspan="4">Attraction to Textbook Conversation</th><th colspan="4">Attraction to Spoken BNC2014 sample</th></tr><tr><th>Verb</th><th>Observed Freq.</th><th>Expected Freq.</th><th>CL</th><th>Verb</th><th>Observed Freq.</th><th>Expected Freq.</th><th>CL</th></tr></thead><tbody><tr><td>WEAR</td><td>71</td><td>42.5</td><td>10.06</td><td>SAY</td><td>153</td><td>91</td><td>21.84</td></tr><tr><td>LOOK</td><td>92</td><td>61</td><td>8.26</td><td>THINK</td><td>101</td><td>64</td><td>11.08</td></tr><tr><td>WAIT</td><td>50</td><td>33.5</td><td>4.52</td><td>RECORD</td><td>15</td><td>8</td><td>3.59</td></tr><tr><td>PLAY</td><td>56</td><td>40</td><td>3.69</td><td>TRY</td><td>98</td><td>76.5</td><td>3.58</td></tr><tr><td>STUDY</td><td>18</td><td>10.5</td><td>3.14</td><td>GET</td><td>75</td><td>59.5</td><td>2.59</td></tr><tr><td>WRITE</td><td>18</td><td>10.5</td><td>3.14</td><td>PAY</td><td>19</td><td>12</td><td>2.49</td></tr><tr><td>VISIT</td><td>10</td><td>5</td><td>3.01</td><td>BE</td><td>34</td><td>25</td><td>2.13</td></tr><tr><td>SLEEP</td><td>14</td><td>8</td><td>2.69</td><td>GO OFF</td><td>6</td><td>3</td><td>1.81</td></tr><tr><td>LISTEN</td><td>28</td><td>19</td><td>2.61</td><td>SWEAT</td><td>5</td><td>2.5</td><td>1.51</td></tr><tr><td>FEEL</td><td>34</td><td>24</td><td>2.58</td><td>PUT</td><td>15</td><td>10.5</td><td>1.41</td></tr><tr><td>SMILE</td><td>11</td><td>6</td><td>2.50</td><td>LOOK FOR</td><td>22</td><td>16.5</td><td>1.40</td></tr><tr><td>MEET</td><td>13</td><td>7.5</td><td>2.44</td><td>STRUGGLE</td><td>6</td><td>3.5</td><td>1.21</td></tr><tr><td>DANCE</td><td>8</td><td>4</td><td>2.41</td><td>CONSIDER</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>PREPARE</td><td>8</td><td>4</td><td>2.41</td><td>CUT</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>ENJOY</td><td>17</td><td>11</td><td>2.08</td><td>DEAL</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>SWIM</td><td>9</td><td>5</td><td>1.97</td><td>EARN</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>WALK</td><td>27</td><td>19.5</td><td>1.94</td><td>GO DOWN</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>STAND</td><td>19</td><td>13</td><td>1.85</td><td>LOOK UP</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>STAY</td><td>25</td><td>18</td><td>1.85</td><td>PICK</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>HURT</td><td>5</td><td>2.5</td><td>1.51</td><td>MAKE</td><td>42</td><td>35.5</td><td>1.12</td></tr><tr><td>PASS</td><td>5</td><td>2.5</td><td>1.51</td><td>GIVE</td><td>10</td><td>7</td><td>1.05</td></tr></tbody></table>

The majority of the top collexemes on the Textbook Conversation side belong to either the semantic domain of activity verbs (PLAY, VISIT, STUDY, WRITE, LISTEN, DANCE, SWIM, WALK etc.) or can be qualified as descriptive verbs (WEAR, SMILE, FEEL etc.). The first domain corresponds to a highly prototypical use of the progressive, often the very first one to be introduced in EFL textbooks – as illustrated with extracts from the grammar sections of two beginner textbooks in (90) and (91).

(90) I am listening to the radio. You are doing your homework. He is looking at a magazine. She is watching a game on TV. It is starting right now. We are making a poster. You are having a nice time. They are winning now.

Du bildest die Verlaufsform so: Form von to be (am, is, are) Vollverb (Infinitiv ohne to) ing-Endung: She is watching TV.

Mit der Verlaufsform der Gegenwart kannst du ausdrücken, dass jemand gerade dabei ist, etwas zu tun. Du kannst das present progressive auch verwenden, wenn du sagen möchtest, dass ein Vorgang gerade abläuft und noch nicht abgeschlossen ist. <TEC: Green Line 1>
(91) To talk about things that are happening now. E.g. I'm reading on the roof. <TEC: English in Mind Starter>

The second domain is less commonly the subject of explicit instruction, though one French textbook series does introduce the progressive with the following function and example sentence:

(92) Description of an activity: *The ghosts are playing football.*
<TEC: Piece of Cake 6>e>

Moreover, picture description tasks are heavily relied on in EFL textbooks as a means of enabling learners to practise using the progressive. Indeed, over the course of the manual annotation process, it was also noted that some 80 occurrences of the present progressive in the Textbook Conversation subcorpus were part of picture or film descriptions, e.g., (93)–(94). In many instances, these sentences are acting as a model for picture/film description tasks set at a later stage in the textbooks (94). This task-effect likely contributes to the high CL scores of verbs such as WEAR, SMILE, FEEL, ENJOY and LOOK in Textbook Conversation.

(93) On the cover, there is a big woman wearing goggles, a type of protection glasses, and a shield. She **is wearing** pins for victory and blood donations on her jacket. She **is also wearing** overalls and there is a massive riveting gun on her knees. [...] But she's also feminine because **she's wearing** lipstick. [...] **She's wearing** nail polish and has curly red hair. <TEC: Piece of Cake 3>e>

(94) The photo shows five people in a dinghy. They're white-water rafting on a river. In the top left corner of the photo, there is a man leaning out of the boat. In the foreground, there's a lot of water. The man at the back of the dinghy is trying to guide the dinghy with his paddle. The other four people **aren't helping** very much. It looks as if they're new to it. They're all **wearing** the same life jackets and helmets. I imagine they're doing this as a holiday activity. <TEC: Pre-intermediate Solutions>

EFL learners are known to frequently overuse BE in the progressive and, indeed, this is considered to be a typical error in many EFL learning contexts. Thus, textbooks frequently include BE in their lists of stative verbs that are normally not to be used in the progressive. It therefore comes as no surprise that, in the progressive, BE is more strongly associated with the Spoken BNC2014 (CL = 2.13), whilst it is strongly repelled by Textbook Conversation. Across the entire Spoken BNC2014 some of the top adjective collocates immediately to the right of the verb form *being* include *able*, *funny*, *sick*, *serious*, *silly*, *rude* and *gay*. Of these, only *being silly* is featured in Textbook Conversation (n = 3), thus it may be postulated that, in spite of being featured in many bestselling EFL grammars (e.g., Murphy et al. 2009: 8), this specific use of the progressive BE + *adjective* (usually describing human behaviour) is in fact underrepresented in Textbook Conversation.
Interestingly, the second highest collexeme in the Textbook Conversation ranking in Table 26 is LOOK, a verb on which Römer (2006b) dedicated an entire article, hereby pointing to some striking differences between the functions of LOOK in the progressive in textbook dialogues and in natural spoken English. Römer (2006b) identified LOOK AT as an overused verb-preposition collocation in the dialogues of an older version of the German textbook series *Green Line*. By teasing out the different prepositional and phrasal verbs associated with LOOK from this analysis (see 4.2.3.1), it can be concluded that this overuse of LOOK in the progressive in Textbook Conversation is most strongly linked to the senses SEE and APPEAR, as opposed to LOOK FOR (= SEARCH), whose progressive form is more attracted to the Spoken BNC2014, and LOOK AFTER, LOOK FORWARD, etc., that do not feature in Table 26 at all.

The top-ranking collexemes from the Spoken BNC2014 also feature a number of activity verbs, but these are subject to corpus topic effects and thus quite different in terms of semantic domains. Unsurprisingly, whilst many of the activities from the Textbook Conversation side of Table 26 are typical school or teenager activities (e.g., PLAY, SLEEP, STUDY, SWIM, WRITE, VISIT, DANCE, LISTEN), the Spoken BNC2014 side features more activities from the world of work and, more generally, adulthood (e.g., PAY, DEAL, EARN). Broadly speaking, it would appear that the world portrayed in school EFL textbooks is seemingly a largely positive, upbeat affair whilst the naturally occurring conversations from the Spoken BNC2014 also feature more nuanced progressive collexemes such as TRY, SWEAT and STRUGGLE.

The fact that the verb lemma RECORD is the third highest ranking collexeme for the Spoken BNC2014 is clearly an artefact of the data collection process. Indeed, the new Spoken BNC was created by asking members of the public to record their conversations on their own smartphones (Love et al. 2017: 329). This unusual set-up prompted regular remarks by the interlocutors of the conversations featuring RECORD in the progressive such as (95) and (96). At the end of the day, the high CL value of RECORD serves as a reminder of the inherent difficulty of obtaining genuinely “authentic” or “naturally occurring” speech data.

(95) oh you're still oh er you're recording? oh you're not recording this?
yes yeah it's perfect perfect talk <BNC2014: SAUR>

(96) oh no no don't don't that was it recording when I told you to F off?
yes yeah but it's fine it doesn't matter <BNC2014: SAZX>

Looking at the highest ranking collexemes from the Spoken BNC2014 half of Table 26, another semantic domain emerges: SAY, THINK, CONSIDER, and in many cases, TRY all function as communication or discourse verbs. As illustrated in (97)–(99), these verbs frequently occur in relatively fixed phrasemes which help to structure discourse. We may speculate that if learners are not exposed to these phrasemes via their
textbooks, they may have difficulty interacting in genuine conversation settings where they are so frequent.

(97) cos **she was saying** um oh I just want to be on the boat <BNC2014: SBYQ>

(98) I suppose we might yeah that's I **was thinking** just go for coffee or something somewhere <BNC2014: SCWC>

(99) yeah like I'm **trying to say** really is that if had plenty of time <BNC2014: SBG4>

Table 27 lists the verb lemmas most strongly associated with the progressive in Textbook Fiction texts as compared to the Youth Fiction corpus. When interpreting these results, it is important to remember that the CL values from Table 26 cannot be compared to those in Table 27 because the total number of progressives investigated in each table differs and, since CL is calculated on the basis of *p*-values, it correlates with sample size. This notwithstanding, the results presented in Table 27 still suggest that there are considerably fewer major discrepancies in the verbs featured in the progressive in Textbook Fiction and the reference Youth Fiction corpus.

<table><caption>Table 27: Top 10 Co-varying Collexemes for ‘Verb’ + ‘Variety’ (Textbook Fiction vs. Youth Fiction sampled)</caption><thead><tr><th colspan="4">Attraction to Textbook Fiction</th><th colspan="4">Attraction to Youth Fiction sample</th></tr><tr><th>Verb</th><th>Obs.<br>Freq.</th><th>Exp.<br>Freq.</th><th>CL</th><th>Verb</th><th>Obs.<br>Freq.</th><th>Exp.<br>Freq.</th><th>CL</th></tr></thead><tbody><tr><td>WALK</td><td>39</td><td>21.5</td><td>7.9</td><td>BE</td><td>15</td><td>9</td><td>2.43</td></tr><tr><td>WAIT FOR</td><td>20</td><td>10</td><td>6.05</td><td>TAKE</td><td>12</td><td>7</td><td>2.2</td></tr><tr><td>SIT</td><td>61</td><td>42</td><td>4.8</td><td>HOLD</td><td>17</td><td>11</td><td>2.08</td></tr><tr><td>WEAR</td><td>37</td><td>25</td><td>3.37</td><td>LIE</td><td>18</td><td>12.5</td><td>1.67</td></tr><tr><td>VISIT</td><td>8</td><td>4</td><td>2.41</td><td>BEGIN</td><td>16</td><td>11</td><td>1.59</td></tr><tr><td>DO</td><td>82</td><td>69</td><td>1.84</td><td>HUNT</td><td>5</td><td>2.5</td><td>1.51</td></tr><tr><td>BURN</td><td>6</td><td>3</td><td>1.81</td><td>GIVE</td><td>9</td><td>5.5</td><td>1.49</td></tr><tr><td>EAT</td><td>11</td><td>7</td><td>1.55</td><td>GROW</td><td>6</td><td>3.5</td><td>1.21</td></tr><tr><td>LOOK FOR</td><td>20</td><td>14.5</td><td>1.52</td><td>FIGHT</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>START</td><td>14</td><td>9.5</td><td>1.50</td><td>WORRY</td><td>4</td><td>2</td><td>1.20</td></tr><tr><td>WRITE</td><td>9</td><td>5.5</td><td>1.49</td><td>STARE AT</td><td>7</td><td>4.5</td><td>1.05</td></tr></tbody></table>

It is tempting to identify similar topic effects to those described in the previous section on conversation; however, the raw frequencies of even the highest ranking collexemes make it clear that no significant discrepancies can be observed in the lexis of progressive verbs between Textbook Fiction and reference Youth Fiction. Nevertheless, this CovCA potentially points to two interesting phenomena. First, as in the conversation CovCA (see Table 26), BE is among the verbs in the progressive that are most strongly associated with the reference Youth Fiction corpus rather than with Textbook Fiction. Second, we turn to the highest ranking verb on the Textbook
Fiction side of the table: WALK. A qualitative analysis of its concordance lines reveals that it occurs in the progressive almost exclusively in framing contexts, e.g., (100)–(102). Framing is another prototypical function of the progressive, which has, however, been shown to be considerably less frequent in authentic usage than EFL grammars and textbooks would suggest (see 4.3.3.5 and Römer 2005: 267).

(100) Sam, Lucy, Leo and Maya **were walking** to the Broadway when someone suddenly shouted to them from a second-floor window. <TEC: Access 2>

(101) I **was walking** in the churchyard when suddenly a man jumped up from among the graves. <TEC: Join the Team 4>

(102) While you **are walking** through the school building, you hear the principal's voice. <TEC: Green Line 4>

The following section presents two further CovCAs: zooming out from the lexical level to focus on the semantic domains distinctively associated with or repelled by Textbook Conversation and Textbook Fiction texts.

### 4.3.4.2 Co-varying Collexeme Analyses of ‘Semantic domain’ + ‘Variety’

Before presenting the results of this second set of CovCAs on the semantic domains of verbs in the progressive, we briefly examine the overall distributions of semantic categories across the four corpora (see Fig. 14). Note that in both Fig. 14 and in the following CovCAs, the verbs categorised as ‘other/unclear’ have been excluded. This category accounts for the vast majority of polysemous verbs (e.g., GIVE which can be an ‘activity’ verb in *to give a present*, a ‘mental’ one in *to give proper consideration*, a ‘causation’ one in *to give cause*, etc.).

The distribution of semantic domains over the four corpora (see Fig. 14) broadly corresponds to what has been reported in prior research on the progressive: namely that the ‘activity’ domain accounts for around half of all progressive occurrences and that the ‘aspectual’ and ‘causative’ domains make up the smallest proportions of progressive occurrences (e.g., Deshors 2017: 273–274; Edwards 2014b: 173).
<figure><img src="image_17.png" /><figcaption>Fig. 14: Distribution of the semantic categories of verbs in the progressives across the four (sub)corpora (excluding polysemous verbs).</figcaption></figure>

This second set of CovCAs makes use of the semantic domain annotation of the verbs of the progressive concordance lines under study in an attempt to identify domains that are specifically attracted to or repelled by the dialogues and narrative texts of EFL Textbooks (see Tables 28 and 30). As in 4.3.4.1, two separate analyses, one for each register, were conducted.

Table 28 presents the results of the semantic CovCA comparing the verb lemmas featured in the progressive in Textbook Conversation with those found in the Spoken BNC2014. The highest CL value in Table 28 is assigned to the ‘communication’ domain, which is strongly attracted to the Spoken BNC2014; in other words, strongly repelled by Textbook Conversation. This seems to confirm the phenomenon inferred from the previous lexis-based CovCAs where a number of ‘communication’ verbs such as SAY and CONSIDER ranked within the 20 progressive collexemes most repelled by Textbook Conversation compared to the Spoken BNC2014.
<table><caption>Table 28: Results of the CovCA ‘Semantic domain’ + ‘Variety’ (Textbook Conversation vs. reference Spoken BNC2014 sample)</caption><thead><tr><th>Semantic domain</th><th>Attraction to</th><th>Obs. freq.</th><th>Exp. freq.</th><th>CL</th></tr></thead><tbody><tr><td>Communication</td><td>Spoken BNC2014</td><td>370</td><td>315.41</td><td>5.99</td></tr><tr><td>Activity</td><td>Textbook Conversation</td><td>1003</td><td>949.67</td><td>3.90</td></tr><tr><td>Existence/relationship</td><td>Textbook Conversation</td><td>73</td><td>60.63</td><td>1.88</td></tr><tr><td>Mental/state</td><td>Spoken BNC2014</td><td>239</td><td>221.23</td><td>1.40</td></tr><tr><td>Causation</td><td>Textbook Conversation</td><td>19</td><td>16.30</td><td>0.66</td></tr><tr><td>Occurrence</td><td>Textbook Conversation</td><td>115</td><td>110.56</td><td>0.54</td></tr><tr><td>Aspectual</td><td>Spoken BNC2014</td><td>25</td><td>24.53</td><td>0.30</td></tr></tbody></table>

The difference with the previous lexis-based CovCA, however, is that this ‘Semantic domain’ + ‘Variety’ CovCA includes a total of 49 ‘communication’ unique verb lemmas identified in the Spoken BNC2014 concordance samples. The most frequent ones are listed in Table 29. A total of 370 progressive concordance lines from the Spoken BNC2014 were annotated as having a verb within the semantic domain of ‘communication’. Bearing this total in mind, we can see from Table 29 that two-thirds of these feature the verbs SAY and TALK. We can therefore speculate that this effect may actually be more lexical (i.e., largely driven by SAY, since TALK is not featured in Table 26) than semantic in nature.

<table><caption>Table 29: Eight most frequent ‘communication’ verbs in the Spoken BNC2014 progressive concordance sample (n = 370)</caption><thead><tr><th>Communication verbs</th><th>Freq. in Spoken BNC2014 concordance sample</th></tr></thead><tbody><tr><td>SAY</td><td>153</td></tr><tr><td>TALK</td><td>93</td></tr><tr><td>TELL</td><td>27</td></tr><tr><td>JOKE</td><td>8</td></tr><tr><td>SPEAK</td><td>7</td></tr><tr><td>TEACH</td><td>7</td></tr><tr><td>ASK</td><td>6</td></tr><tr><td>KID</td><td>6</td></tr></tbody></table>

In both ‘Semantic domain’ + ‘Variety’ CovCAs the highest ranking CL values for the textbook registers are those of the ‘activity’ domain. As hypothesised in 4.3.4.1 on the lexical CovCAs, this may be due to the prototypical functions of the progressive emphasised in the textbook grammar sections, as well as the description tasks intended to allow learners to practise using the progressive, both of which are modelled in a number of textbook dialogues.
<table><caption>Table 30: Results of the CovCA of ‘Semantic domains’ + ‘Fiction Varieties’ (Textbook Fiction vs. Youth Fiction sample)</caption><thead><tr><th>Corpus</th><th>Semantic domain</th><th>Obs. freq.</th><th>Exp. freq.</th><th>CL</th></tr></thead><tbody><tr><td>Occurrence</td><td>Youth Fiction Sampled</td><td>135</td><td>110.42</td><td>3.47</td></tr><tr><td>Activity</td><td>Textbook Fiction</td><td>688</td><td>648.74</td><td>3.16</td></tr><tr><td>Mental</td><td>Youth Fiction Sampled</td><td>180</td><td>162.41</td><td>1.68</td></tr><tr><td>Existence/Relationship</td><td>Textbook Fiction</td><td>65</td><td>59.07</td><td>0.82</td></tr><tr><td>Aspectual</td><td>Youth Fiction Sampled</td><td>31</td><td>27.73</td><td>0.64</td></tr><tr><td>Causation</td><td>Youth Fiction Sampled</td><td>11</td><td>8.910</td><td>0.64</td></tr><tr><td>Communication</td><td>Textbook Fiction</td><td>175</td><td>172.66</td><td>0.38</td></tr></tbody></table>

Furthermore, the results summarised in Table 30 show that the semantic domain ‘occurrence’ is significantly more strongly attracted to the Youth Fiction reference corpus than to Textbook Fiction. The high lexical diversity of fictional writing means that this particular effect is undeniably semantic rather than lexical. Indeed, the most frequent ‘occurrence’ verb within the Youth Fiction concordance lines, go on, has a raw frequency of just 18 out of 135 concordance lines with ‘occurrence’ verbs. Further, qualitative examination of the Youth Fiction concordance lines featuring ‘occurrence’ verbs reveals that this attraction of the domain ‘occurrence’ to the reference corpus is the result of longer, more detailed descriptions in the novels of the Youth Fiction corpus. In contrast, the narrative texts in the textbooks focus more on the actions of characters and thus the activity domain is more strongly attracted to Textbook Fiction.

### 4.3.4.3 Correspondence Analysis (CA) of ‘Semantic domain’ + ‘Variety’ + ‘Register’

This section presents the results of a simple binary CA with ‘Semantic domain’ and ‘Corpus’ as the contributing variables and ‘Register’ and ‘Variety’ as supplementary variables. The $\chi^2$ test of independence between the two main variables returned 107.65 ($p < 0.001$), thereby indicating that there is indeed a significant relationship between the two variables. However, the total inertia of the contingency table is very low ($\Phi^2 = 0.019$). As a result, the data points are all relatively close to the point where the average ‘Corpus’ and ‘Semantic domain’ profiles coincide, which is visualised as the origin of the biplot (see Fig. 15, note the small range on both axes of the biplot indicating low degrees of maximum independence on both dimensions 1 and 2).

The intensity of the relationship between ‘Semantic domain’ and ‘Corpus’ is very weak (Cramér’s $V = 0.08$). This was to be expected because, as shown in previous research (Deshors 2017), the progressive as a construction is associated with broadly similar distributions of semantic domains across registers and varieties. Nevertheless,
CA helps us tease out subtle differences between the profiles of the variables under investigation.

Fig. 15 displays the first two dimensions of the CA results which, together, account for 95.93% of the total inertia of the data. The supplementary variables (which do not contribute to the profiles) are labelled in dark red. The categories plotted furthest away from the axes are the most “different” from the average profiles, in other words, most independent.

<figure><img src="image_18.png" /><figcaption>Fig. 15: Simple binary correspondence analysis of ‘Semantic domain’ and ‘Corpus’</figcaption></figure>

Starting with Dimension 1, which explains the most variance, we observe a distinction between the Spoken BNC2014 on the negative end of the scale, and the Youth Fiction corpus on the other. Though following the same register pattern, the textbook registers are much closer to the *x*-axis and this reflects less pronounced differences in the distribution of semantic domains between the two textbook registers. The semantic category ‘communication’ clusters with the Spoken BNC2014, while ‘occurrence’ and ‘aspectual cluster’ with Youth Fiction.

While Dimension 1 clearly reflects the distinction of register, the spread of corpus points along Dimension 2 shows a distinction between the reference registers (positive
values) and the textbook ones (negative values). The semantic domain ‘activity’
clearly clusters with both Textbook Conversation and Textbook Fiction, whereas
progressives with ‘mental’ verbs are less associated with Textbook English and more
with the reference ENL corpora.

4.3.4.4 Distinctive Collexeme Analyses

All the analyses presented thus far have only considered progressive constructions,
comparing the morphosyntactic, functional, lexical and semantic peculiarities of
progressives featured in Textbook Conversation and Textbook Fiction as compared
to two samples of naturally occurring texts of the same registers. In the following, by
contrast, the lexis of progressive constructions will be examined through the lens of
the progressive vs. non-progressive alternation by comparing the results of two
independent Distinctive Collexeme Analyses (DCAs) for each register under study.

Table 31 presents the results of the DCAs for the comparison Textbook English vs. naturally occurring ENL conversation. In terms of absolute difference, by far the largest difference reported in Table 31 concerns the stative verb BE at the bottom of the table. It is worth noting that the direction of the difference contradicts the findings reported in 4.3.4.4 and Table 32. As a reminder, we observed that, when comparing solely the verbs that occur in the progressive in either textbook or ENL registers, BE appears to be under-represented in both Textbook Conversation and Textbook Fiction compared to their respective reference registers. However, when comparing the number of occurrences of BE as a main verb in FVPs in either progressive or non-progressive constructions (as in Table 31), it transpires that BE is in fact less attracted to the progressive in the Spoken BNC2014 than in Textbook Conversation. This is likely due to the considerably higher frequency of BE as a main verb in FVPs in naturally occurring conversation than in textbook dialogues (66,344 occurrences of BE as a verb pmw in the Spoken BNC2014 vs. 47,399 pmw in Textbook Conversation). The present result thus confirms the indispensability of combining several analysis methods to answer any one research question.
<table><caption>Table 31: Most significantly distinctive collexemes associated with the progressive (positive G² values) and the non-progressive constructions (negative G² values) in Textbook Conversation and the Spoken BNC2014 sample</caption><thead><tr><th colspan="4">Textbook Conversation</th><th colspan="4">Spoken BNC2014 sample</th></tr><tr><th>Lemma</th><th>Non-prog (O:E)</th><th>Prog (O:E)</th><th>CL</th><th>Lemma</th><th>Non-prog (O:E)</th><th>Prog (O:E)</th><th>CL</th></tr></thead><tbody><tr><td>WEAR</td><td>71:7.2</td><td>99:162.8</td><td>227.8</td><td>TALK</td><td>134:14</td><td>95:215.9</td><td>464.5</td></tr><tr><td>TALK</td><td>88:12.5</td><td>205:280.5</td><td>218.0</td><td>TRY</td><td>127:18</td><td>172:281.4</td><td>337.1</td></tr><tr><td>DO</td><td>206:74</td><td>1527:1659</td><td>177.8</td><td>DO</td><td>333:182</td><td>2766:2917.4</td><td>116.1</td></tr><tr><td>LOOK</td><td>117:44</td><td>921:994</td><td>90.3</td><td>SAY</td><td>214:110</td><td>1655:1759</td><td>87.2</td></tr><tr><td>WAIT</td><td>50:10</td><td>188:228</td><td>88.1</td><td>GO</td><td>325:193</td><td>2960:3092</td><td>86.6</td></tr><tr><td>GO</td><td>205:105</td><td>2263:2363</td><td>83.4</td><td>JOKE</td><td>15:0.9</td><td>0:14</td><td>85.1</td></tr><tr><td>TRY</td><td>55:13</td><td>253:295</td><td>81.1</td><td>LOOK</td><td>101:39</td><td>556:619</td><td>77.3</td></tr><tr><td>KID</td><td>13:0.7</td><td>3:15.3</td><td>67.0</td><td>WORK</td><td>56:18</td><td>243:282</td><td>59.0</td></tr><tr><td>WORK</td><td>50:14</td><td>279:315</td><td>60.2</td><td>COME</td><td>102:47</td><td>695:751</td><td>53.6</td></tr><tr><td>PLAY</td><td>56:18</td><td>353:392</td><td>58.4</td><td>SIT</td><td>36:9</td><td>113:140</td><td>53.4</td></tr><tr><td>PLAN</td><td>17:2</td><td>26:41</td><td>52.0</td><td>RECORD</td><td>15:2</td><td>11:5</td><td>50.4</td></tr><tr><td>JOKE</td><td>11:1</td><td>5:15</td><td>50.1</td><td>KID</td><td>8:1</td><td>0:7.5</td><td>45.4</td></tr><tr><td>RAIN</td><td>12:1</td><td>9:20</td><td>47.9</td><td>PLAN</td><td>12:1</td><td>9:20</td><td>41.0</td></tr><tr><td>STAND</td><td>20:4</td><td>67:83</td><td>38.5</td><td>RAIN</td><td>8:1</td><td>2:10</td><td>35.1</td></tr><tr><td>STUDY</td><td>18:3</td><td>56:71</td><td>36.5</td><td>PLAY</td><td>30:9</td><td>119:140</td><td>35.0</td></tr><tr><td>LIE</td><td>12:1</td><td>21:32</td><td>34.4</td><td>STRUGGLE</td><td>9:1</td><td>4:13</td><td>34.6</td></tr><tr><td>WALK</td><td>27:8</td><td>151:170</td><td>32.3</td><td>DRINK</td><td>18:4</td><td>43:58</td><td>33.3</td></tr><tr><td>SMILE</td><td>11:5</td><td>21:30.6</td><td>30.2</td><td>WAIT</td><td>22:6</td><td>71:88</td><td>31.7</td></tr><tr><td>PREPARE</td><td>8:1</td><td>9:16.3</td><td>27.8</td><td>WEAR</td><td>18:4</td><td>49:63</td><td>30.0</td></tr><tr><td>THINK</td><td>27:68</td><td>1558:1518</td><td>-33.1</td><td>REMEMBER</td><td>1:189</td><td>311:294</td><td>-29.9</td></tr><tr><td>HAVE</td><td>59:116</td><td>2675:2618</td><td>-37.1</td><td>GET</td><td>121:190</td><td>3113:3045</td><td>-31.9</td></tr><tr><td>LET</td><td>3:33</td><td>775:750</td><td>-47.3</td><td>MEAN</td><td>2:27</td><td>449:425</td><td>-40.2</td></tr><tr><td>LOVE</td><td>0:23</td><td>544:521</td><td>-47.5</td><td>SEE</td><td>11:56</td><td>943:898</td><td>-57.1</td></tr><tr><td>SEE</td><td>8:58</td><td>1350:1300</td><td>-70.8</td><td>HAVE</td><td>94:188</td><td>3102:3009</td><td>-63.2</td></tr><tr><td>WANT</td><td>2:50</td><td>1176:1128</td><td>-86.3</td><td>WANT</td><td>3:52</td><td>873:825</td><td>-83.4</td></tr><tr><td>BE</td><td>16:846</td><td>19876:19047</td><td>-1962.2</td><td>BE</td><td>56:1237</td><td>21020:19839</td><td>-2691.1</td></tr></tbody></table>

The most striking differences between the results of the two progressive vs. non-progressive DCAs are visualised in Fig. 16: each point represents one verb lemma. All verb lemmas are included except BE, which returned very negative CL values in both DCAs (see Table 31). It was therefore removed before the CL values were standardised (to z-scores) to avoid this one verb skewing the results. The scatterplot makes clear that the CL values that emerged from the two DCAs are very highly correlated (Pearson's r(447) = 0.98, p < 0.001). We may therefore conclude that there are only few verbs that are considerably more or less attracted to the progressive in Textbook Conversation than in the Spoken BNC2014 sample. The labelled points on Fig. 16, however, correspond to the twenty lemmas with the greatest absolute differences in standardised CL values. The rest of the analysis will focus on these verbs.
<figure><img src="image_19.png" /><figcaption>Fig. 16: Comparison of the standardised CL values (scaled and centred $G^2$) returned by the two progressive vs. non-progressive Distinctive Collexeme Analyses (DCAs). Positive values represent attraction to the progressive, negative to the non-progressive construction.</figcaption></figure>

We will begin by examining the bottom left quadrant with negative standardised $G^2$ values. These points correspond to verb lemmas repelled by the progressive construction in both corpora. The labelled points in this quadrant are all found to the left of the regression line. These are stative verbs and verbs of involuntary perception that textbooks frequently list as “exception” or “state verbs” that cannot be used in the progressive (see, e.g., Niemeier 2017: 109), e.g., LOVE, LET (in the sense of ALLOW), SEE (as a verb of involuntary sensory perception) and WANT. However, the present results show that, in natural spoken conversation, stative verbs, in particular, do not occur in the progressive quite as rarely as EFL grammars would suggest, e.g., (103)–(106).

(103) so I ended up going and babysitting well the daughter's probably eleven
right
something like that erm so I was kind of letting her be in charge you
know <BNC2014: S8BQ>

(104) why don't you let me buy a b Park Lane?
no I'm not letting you buy Park Lane <BNC2014: SXK5>
(105) and yeah she was like oh she she she **was loving** she **was loving** the fact
she was being recorded though like <BNC2014: S6BS>

(106) yeah **are** you **wanting** that tape back?
no
are you sure?
yeah you leave it here <BNC2014: S2KP>

Among the verbs that are displayed to the left of the regression line on Fig. 16, we
find RECORD, which would suggest that it is considerably more attracted to the
progressive in natural conversation than in textbook dialogues. Its presence on this
side of the plot has, however, already been identified as a corpus artefact resulting
from the data-collection process of the Spoken BNC2014 (see 4.3.4.1 and excerpts
(95) and (96)).

The remaining verb lemmas to the left of the regression line on Fig. 16 confirm another finding from the previous analyses: TALK, SAY, THINK and, in many cases, also TRY, are communication verbs, which were already shown to be more frequent in the progressive in natural conversation than in textbook dialogues. Whilst this DCA comparison confirms the findings of the lexical and semantical CovCAs in 4.3.4.1 and 4.3.4.2, as well as those from the CA in 4.3.4.3, it is interesting to note that the rank order of the most mis-represented lexemes is different. SAY is no longer the most under-represented communication verb in the progressive, but rather TALK and TRY.

Among the labelled verb lemmas in the bottom left quadrant of Fig. 16, we also find
THINK, which constitutes a special case. It is repelled by the progressive construction
in the textbook conversation corpus, but shows no significant association to either
the progressive or the non-progressive construction in the Spoken BNC2014 sample.
A qualitative exploration of the corresponding concordance lines shows that, in EFL
textbooks, THINK is primarily used in non-progressive constructions to give opinions
(107)–(108). In addition, textbook dialogues sometimes feature THINK to express
doubt (109) or hedge statements (110).

(107) Jack: What about a book? Oliver: No, I **don't think** that's a good idea.
<TEC: Access G 1>

(108) I didn't like Celtic music before because I **thought** it was too fast and repetitive. <TEC: Hi There 6>

(109) Frank Sinatra? Did he write the song?
Hmmm... **I think** so...? I know he sang it. <TEC: Piece of Cake 5>

(110) Hi, it's me. Where are you? - Right. Listen! I'm at home. Why don't you come over? There's something interesting, I **think**. <TEC: Hi There 6>

There is no doubt that these functions are also very common in naturally occurring
speech. The qualitative concordance analysis revealed that the difference observed in
Fig. 16 is, in fact, foremost driven by just one progressive discourse-structuring
phraseme: *I'm/was thinking* (111), which is almost ten times more frequent in the Spoken BNC2014 (1,899.6 occurrences per 10,000 words in the Spoken BNC2014 [*n*= 2,444]) than in Textbook Conversation (196.7 per 10,000 words [*n* = 10]).<fnref n="29" />

(111) Stockholm
Copenhagen
Copenhagen yeah I haven't really been to Scandinavia
that's really funny cos I was thinking that as well I was thinking if
grandad was here
yeah <BNC2014: S6JP>

Like other phrasemes featuring communication verbs in the progressive, in naturally occurring conversation, *I was thinking* frequently serves to frame discourse (112)– (113).

(112) no I don't think he's in quite that state yet... anyway and then um... what else **what [is] he saying?** Oh I said **I was telling** him about John and he was like Oh we should go there on like me you and Matt... we should go... and **I was thinking** um baby?
then **I was saying we were thinking** of going to Sunderland like in September <BNC2014: SR8N>

(113) erm when I went into the love chart I was hoping I might **I was thinking** you might be at the top but then when but you were at the bottom with Jill <BNC2014 SES2>

By contrast, out of the mere eight occurrences found in textbook dialogues, only three have discourse structuring or softening functions similar to those found in the authentic data (114)–(115).

(114) Kim, I've got an idea, but don't laugh. At school today, **I was thinking:** I'm from Seattle but I've never been to top of the Space Needle.
<TEC: Green Line New 4>

(115) Sounds great! So Cape Town, good. Now would you like to go somewhere else? **I was thinking** that we could spend one week in Cape Town and one week at the Kruger National Park in the North East. <TEC: Piece of Cake 4e>

Conversely, Fig. 16 shows that another verb belonging to the semantic domain of communication, KID, is more strongly associated with the progressive construction in Textbook Conversation than in the Spoken BNC2014 sample (unlike JOKE, which ranks high on both collexeme lists in Table 31). Whilst textbook authors clearly favour the interrogative phraseme *are you kidding (me)?* and the explosive *you're kidding!*, the Spoken BNC2014 also features the non-progressive phrase *I kid you not*. Before arguing for the inclusion of this particular phraseme in textbook dialogues,

<footnote n="29">²⁹ The following Sketch Engine CQL query was used to arrive at these frequencies: [word="I"] [lemma="be"] [lemma!="not"] {0,2} [word="thinking"). It allows for small variations on the phraseme such as *I'm/am/was just/yeah/er/like/kind of thinking*, but excludes negated forms which do not fulfil the same discourse function.</footnote>
however, it should be noted that it is very unevenly dispersed across the Spoken BNC2014: 19 out of the 29 occurrences were uttered by just one speaker (S0439, a 23-year-old female L1 speaker).

In a similar vein, the lemma COME is more strongly attracted to the progressive in naturalistic spoken conversation than in Textbook Conversation as a result of two phrasemes frequently featured in textbook dialogues: *come on!* (116) and the phrasebook classic *I come from [country/town]* (117). It also worth noting that the most frequent one-to-the-right collocates of COME in Textbook Conversation are *on, to, from* and *here*. By contrast, in the Spoken BNC2014, *back, out, in, to* and *up* are most frequently paired with COME and these phrasal verbs are more likely to occur in the progressive, as in (118)–(119).

(116) A rainforest in Cornwall? **Come on**, Lucy, it can't be real... England isn't hot enough for a rainforest. <TEC: Access G 2>

(117) I **come from** South Africa. I'm joyful and friendly.
<TEC: Piece of Cake 5°>

(118) if if your doctor occupational health whoever say it needs to be
February it'll be February but I'm thinking in my mind **I should be**
**coming back** January <BNC2014: SMW8>

(119) better watch the dog now
**are you coming out?**
yeah
we'd better put the dogs on a lead cos the chickens are out there
<BNC2014: S2XJ>

To the right of the regression line in Fig. 16 we find verbs that are more strongly associated with the progressive in Textbook Conversation than in the Spoken BNC2014. The case of KID has already been discussed. As noted in the discussion of the results of the previous analyses (see 4.3.4.1), DO, PLAY and STUDY are instantiations of the kinds of activity verbs that are frequently used by textbook authors as model, prototypical verbs for the progressive construction. Similarly, WEAR, STAND and SMILE have been shown to be frequently featured in textbook dialogues modelling picture description tasks designed to provide learners with the opportunity to practise using the progressive.

The remaining lemma on this side of the scatterplot, WAIT, is also associated with
how the progressive is traditionally taught in EFL textbooks and grammars: textbook
authors make frequent use of WAIT as a means of featuring present and past perfect
progressives in dialogues (63). It is also a useful verb to demonstrate the progressive's
framing function (120), which was shown in 4.3.3.5 to be over-represented in both
textbook Conversation and Fiction.

(120) A full-blown fight broke out between the two groups and someone went to get a teacher. While we **were waiting** out outside the head teacher's
In the following, we turn to the results of the same types of analyses as above, now focusing on the Fiction register. Thus, Table 32 compares the results of two progressive vs. non-progressive DCAs for the fiction register.

<table><caption>Table 32: Top 30 verb lemmas with the greatest absolute difference in attraction/repulsion CL scores to the progressive in Textbook Fiction and the Youth Fiction Corpus (negative values in the second and/or third columns represent repulsion of the progressive, in other words, attraction to non-progressive constructions)</caption><thead><tr><th colspan="4">Textbook Fiction</th><th colspan="4">Youth Fiction sample</th></tr><tr><th>Lemma</th><th>Non-prog (O:E)</th><th>Prog (O:E)</th><th>CL</th><th>Lemma</th><th>Non-prog (O:E)</th><th>Prog (O:E)</th><th>CL</th></tr></thead><tbody><tr><td>SIT</td><td>62:12</td><td>180:230</td><td>120.5</td><td>TALK</td><td>35:6</td><td>85:114</td><td>76.4</td></tr><tr><td>WEAR</td><td>37:4</td><td>49:82</td><td>112.6</td><td>WORK</td><td>23:5</td><td>73:91</td><td>41.2</td></tr><tr><td>TALK</td><td>50:8</td><td>120:162</td><td>110.6</td><td>JOKE</td><td>6:1</td><td>0:6</td><td>36.4</td></tr><tr><td>DO</td><td>82:25</td><td>429:487</td><td>92.3</td><td>TRY</td><td>36:12</td><td>204:229</td><td>36.0</td></tr><tr><td>WAIT</td><td>41:8</td><td>130:163</td><td>74.1</td><td>STAND</td><td>31:10</td><td>176:197</td><td>30.9</td></tr><tr><td>STAND</td><td>35:8</td><td>128:155</td><td>56.0</td><td>PLAN</td><td>8:1</td><td>6:14</td><td>30.0</td></tr><tr><td>WALK</td><td>43:14</td><td>243:272</td><td>43.4</td><td>LIE</td><td>19:5</td><td>77:91</td><td>27.5</td></tr><tr><td>SHINE</td><td>9:1</td><td>5:13</td><td>36.9</td><td>SIT</td><td>27:10</td><td>167:185</td><td>23.9</td></tr><tr><td>TRY</td><td>34:11</td><td>193:216</td><td>34.0</td><td>FIND</td><td>0:13</td><td>259:247</td><td>-25.7</td></tr><tr><td>ASK</td><td>3:24</td><td>503:482</td><td>-31.3</td><td>WANT</td><td>1:18</td><td>372:355</td><td>-29.2</td></tr><tr><td>WANT</td><td>1:28</td><td>588:561</td><td>-49.8</td><td>SEE</td><td>7:33</td><td>679:653</td><td>-31.9</td></tr><tr><td>SEE</td><td>4:38</td><td>794:760</td><td>-53.0</td><td>HAVE</td><td>16:52</td><td>1056:1020</td><td>-36.0</td></tr><tr><td>KNOW</td><td>0:29</td><td>601:572</td><td>-59.8</td><td>KNOW</td><td>1:33</td><td>678:646</td><td>-58.7</td></tr><tr><td>SAY</td><td>24:93</td><td>1907:1838</td><td>-78.7</td><td>SAY</td><td>32:122</td><td>2506:2416</td><td>-104.2</td></tr><tr><td>BE</td><td>3:348</td><td>7227:6883</td><td>-775.3</td><td>BE</td><td>15:334</td><td>6919:6600</td><td>-644.3</td></tr></tbody></table>

In contrast to the Conversation DCAs (Table 31), the Fiction DCA comparison in Table 32 clearly confirms that Textbook Fiction more strongly associates the verb lemma BE with non-progressive constructions than the Youth Fiction corpus. In fact, BE is the lemma with the greatest absolute difference in CL values. This would suggest that BE as a lexical verb in progressive FVPs appears to be under-represented in Textbook Fiction. This is likely to be problematic for EFL learners since the phraseological pattern *BE + being* is known to be subject to specific lexical restrictions and to fulfil distinct communicative and rhetorical functions. In terms of lexicogrammatical patterning, an earlier corpus study of the BNC1994 revealed that the
vast majority of collexemes in the adjective slot of the *BE + being + adjective*
construction (81.21%) are “judgement adjectives” (e.g., *unusual, clever, silly,*
*cautious, irresponsible*) (Kheovichai 2017: 110). Functionally, it would appear that
BE + *adjective* is often employed instead of an action verb in order to “assign an
evaluative meaning to the action” (Kheovichai 2017: 127). Indeed, a qualitative
comparison of random progressive concordance lines with BE from Textbook Fiction
and Youth Fiction suggests that, as already noted in 4.3.4.1, this under-representation
of BE in the progressive in Textbook Fiction is likely to result in learners being
underexposed to the co-occurrences and functional uses of BEING + adjectives
describing temporary and/or uncharacteristic behaviours – such constructions being
highly frequent in Youth Fiction, e.g., (121)–(122).

(121) He said that I was being selfish and that I was never to set foot inside the house again. <Youth Fiction: Haddon 2003: The Curious Incident of the Dog in the Night-Time>

(122) "Well, I don't suppose it matters," sighed Hermione. "Even if he was being honest, I never heard such a lot of nonsense in all my life."

<Youth Fiction: Rowling 2007: Harry Potter and the Deathly Hallows>

Fig. 17 compares the attraction of verb collexemes to the progressive/non-progressive
constructions in Textbook Fiction and Youth Fiction. The circles on or close to the
diagonal line behave similarly in both corpora. As in the corresponding figure for the
conversation register (Fig. 16), verbs that behave most differently are labelled.

<figure><img src="image_20.png" /><figcaption>Fig. 17: Comparison of the standardised CL values (scaled and centred G²) returned by the two progressive vs. non-progressive Distinctive Collexeme Analyses (DCAs)</figcaption></figure>
As was to be expected, ‘communication’ verbs follow different patterns in fiction than in conversation. For instance, SAY is more strongly associated with non-progressive forms in Youth Fiction than in the corresponding textbook register. This is most likely due to the considerably higher occurrence of the form *said* (7,883 occurrences pmw in Youth Fiction compared to 5,539 in Textbook Fiction). TALK, too, follows the opposite pattern to that observed in the two conversation varieties. In Textbook Fiction, it appears to be over-represented in the progressive compared to Youth Fiction. JOKE, on the other hand, follows the trend observed in Textbook Conversation and is seemingly under-represented in the progressive in Textbook Fiction.

Some of the verbs displayed to the right of the regression line in Fig. 17 are frequently used as a framing verb in Textbook Fiction – a function, which was already shown to be over-represented in Textbook Fiction as compared to Youth Fiction (see 4.3.3.5 and 4.3.4.1). The case of WALK was already discussed in 4.3.4.1 (see also excerpts (100)–(102)). Two additional typical ‘framing’ verbs to come out of the analysis displayed on Fig. 17 are SHINE (123) and WAIT (124).

(123) It was not a moment too soon. The fog was rising and the moon was shining on the hills. We heard footsteps and saw a light that went up and down. <TEC: Green Line 3>

(124) When Prince Llewellyn walked through the door, Gelert was waiting for him. <TEC: English in Mind 3>

The verbs WEAR and DO follow the same patterns as in all previous analyses – they are more attracted to the progressive in the two textbook registers than in their respective reference corpora. According to Fig. 17, SIT, however, is more attracted to the progressive in Textbook Fiction than in Youth Fiction, although the opposite was observed for the conversation varieties. In 4.3.4.1, it was speculated that WEAR was strongly attracted to the progressive because textbook authors often rely on picture description tasks to practise the progressive and that these tasks are sometimes (usually implicitly) modelled in textbook dialogues. In the case of narrative texts, however, it would appear that the over-representation of WEAR in the progressive is due to textbook authors having to describe the characters of their stories with the limited vocabulary that learners are familiar with. Clothes being one of the first few topics introduced in most textbook series, WEAR becomes the lexical verb of choice for character descriptions. Similarly, DO is a highly versatile action verb which textbook authors can use to describe many activities that the authors included in the Youth Fiction corpus can depict using a much broader range of lexical verbs. Perhaps the same is true of SIT as a simple description verb introduced early on in textbook series. No functional differences in the use of progressive and non-progressive constructions with SIT could be deciphered from a concordance-based analysis.
Finally, one more lemma on Fig. 17 merits further exploration: HAVE is repelled by the progressive construction in both Textbook Fiction and Youth Fiction but the plot shows that it is significantly more so in the narrative texts of the TEC than in the novels of the Youth Fiction corpus. This finding can be traced to the fact that when HAVE is found as the main verb of a verb phrase in Textbook English, it is most frequently in the sense of possessing something, in which sense progressive constructions are, of course, very rare. By contrast, HAVE in novels is most often used as a light verb and in a wide variety of collocations (125)–(126), which can and frequently are used in the progressive (e.g., *having a nap*, *having a seizure*, *having an argument*, *having a chat/conversation*, *having second thoughts*, *having a hard time*, *having trouble*, etc.). Of those many highly frequent HAVE collocations that are often found in the progressive, the only one featured more than once in Textbook Fiction is *having fun* (126), which, alone, accounts for a third of all HAVE progressive constructions.

(125) Harry rather doubted he would be able to bring off this particular spell; he was still having difficulty with non-verbal spells, something Snape had been quick to comment on in every D.A.D.A. class.

<Youth Fiction: Rowling 2005: Harry Potter and the Half-Blood Prince>

(126) Zoe watched as the dog's enthusiasm quietened. Jake sat on the snow as she snuffled in his ear. It almost seemed to Zoe that the two of them were having a conversation. <Youth Fiction: Joyce 2010: The Silent Land>

(127) Back in the Victorian room, Jay was having fun in front of the camera. He was a Victorian gentleman, and his in the photo was a fine Victorian lady. <TEC: Green Line New 3>

## 4.4 Conclusion

In the following, some of the key findings of this chapter are summarised in an attempt to provide answers to the seven research questions laid out in 4.1.3.

The first aim was to verify whether progressive constructions are genuinely overused in Textbook English. The analysis in 4.3.1 showed that this was not the case for either Textbook Conversation or Textbook Fiction texts. As for the second research question, a fine-grained analysis of the morphosyntactic contexts in which EFL learners encounter progressives via their textbooks revealed a number of notable divergences from the reference corpora, particularly in the comparisons between Textbook Conversation and the Spoken BNC2014 sample.

Starting with the morphosyntactic contexts of progressives, past progressive forms were found to be under-represented in Textbook Conversation, even after making allowances for the late introduction of this tense form in school textbook series (see 4.3.2.1). Overall, Textbook Conversation appears to slightly under-represent contracted forms of the progressive. However, the ratio of contracted to non-
contracted progressives varies markedly across different textbook series. Two French textbook series, *Join the Team* and *Piece of Cake*, substantially under-represent contracted progressives (see 4.3.2.2) in their dialogues. In addition, negated progressives were also seen to be under-represented in both Textbook Conversation and Fiction (see 4.3.2.3). In contrast, progressive in interrogative contexts are seemingly over-represented in Textbook Conversation (see 4.3.2.4). Furthermore, the types of questions featured in textbook dialogues and authentic conversations differ quantitatively, both in terms of syntax and rhetorical functions (see 4.3.2.4). Though the proportions of active and passive progressives do not significantly differ across the textbook and ENL registers, the Spoken BNC2014 features more colloquial GET and HAVE progressive passives, whereas Textbook Conversation exclusively features *being* + *V<sub>ed</sub>* passive constructions that are more typical of written registers than spontaneous speech (see 4.3.2.5).

The third research question concerned the functions of progressives in the two Textbook English registers. Overall, tenses and time references are distributed similarly across the textbook registers and their corresponding reference corpora when taking the effect of the gradual increase in complexity across textbook levels into account. Still, the results show that progressives in Textbook Conversation less frequently refer to generally valid statements and hypothetical situations than in authentic conversation (see 4.3.3.1). Furthermore, future time references are proportionally more frequently expressed with a present progressive. Modal constructions – foremost WILL + *be* + V<sub>ing</sub> –, on the other hand, are under-represented compared to the Spoken BNC2014 (see 4.3.3.2). Additional functions of progressive constructions such as referring to repeated actions, non-continuous actions and gradual change or development are also under-represented in both textbook registers (see 4.3.3.3–4.3.3.5). Progressives used in framing contexts, however, are considerably over-represented in textbook registers, in particular in Textbook Conversation as this function was shown to be very rare in the transcripts of the Spoken BNC2014 (see 4.3.3.5).

The fourth research question focused on Textbook Conversation, asking whether the progressives featured in the dialogues of modern textbooks are now more representative of present-day authentic ENL conversation than was the case at the time of Römer’s (2005) study. Whilst some of the trends were confirmed, others were not. For instance, whereas Römer (2005: 244) reported that the present progressive, was overused in her corpus of textbook dialogues, 4.3.2.1 made clear that this trend is only true of the beginner textbooks. Furthermore, it was suggested that there is good pedagogical reasoning behind this choice because present progressives are by far the most frequent form of progressives in Spoken English. Similarly, perfect progressives were not found to be under-represented in the present Textbook Conversation subcorpus. However, past progressives are still under-represented across
all levels of textbook dialogues. Overall, contracted progressives seem to no longer be
as under-represented as they were at the time of Römer's study; however,
interestingly, the two most recent versions of the German textbook series *Green Line*
/pub. 2006-2009 and 2014-2018) still appear to under-represent contracted
progressives (see Table 17), though noticeably less so than in the older version
examined by Römer (pub. 1995-2000, see Römer 2005: 245). Likewise, though negated
progressives remain marginally less frequent in textbook dialogues than in naturally
occurring British English conversation (see 4.3.2.3), the difference is not as striking
as in Römer's data (2005: 250). Contrary to expectations, the more recent series from
Klett, *Green Line New*, features the second lowest proportion of negated progressives
(3.65%) in its dialogues, whereas the older version, *Green Line*, has the highest
proportion, 7.19%, which is remarkably close to that observed in the Spoken BNC2014
reference corpora. As in Römer (2005: 252), progressives in interrogative contexts
were found to be over-represented in textbook dialogues.

Turning to the function of progressives, Römer (2005: 262) observed that progressives
that express repeatedness were comparatively rare in her textbook dialogues (9.12%
and 9.87%, compared to 35.05% and 38.64% in her reference ENL data) and argued
that, since she identified repeatedness as one of the core functions of the progressive,
it should be more frequently and prominently featured in Textbook English (Römer
2005: 284). The results presented in 4.3.3.4 (see Table 24) appear to show that, whilst
the repeated function of progressives is now better represented in modern textbooks,
it remains underused. Although 'non-continuous' progressives were even rarer in the
older textbook dialogues (2.53% and 4.56%), for this functional feature, too, we can
confirm the tendency observed by Römer (2005: 261) (see 4.3.3.3). Finally, like Römer
(2005: 267), we still observe a strong tendency to emphasise the framing function of
the progressive in textbook dialogues, in spite of the fact that it is quite atypical of
spoken English (see 4.3.3.5).

To answer the fifth research question, three types of collostructional analyses were
conducted and compared. The ensuing results and discussion thereof demonstrated
the benefit of using a range of methodological approaches to tease out relevant
patterns and avoid biases (see Deshors 2017 for a similar plaidoyer). Verbs such as
WEAR and LOOK were found to be consistently over-represented in the progressive, as
were verbs associated with the framing function, e.g., WALK and WAIT. Unsurprisingly,
stative verbs and other verbs typically taught as not (usually) occurring in the
progressive are more strongly associated with non-progressive constructions in the
textbook registers than in the respective reference corpora (e.g., BE, LIKE, LOVE).

The CovCAs and CA conducted in an attempt to answer the sixth research question
revealed some interesting patterns as to the semantic domains of the verbs featured
in the progressive in the two textbook registers. Although the distributions of
semantic domains was broadly similar across all four (sub)corpora under study (see 4.3.4.1), subtle differences of pedagogical relevance were identified. For instance, ‘activity’ verbs are more associated with textbook registers, whereas ‘mental/state’ verbs are more associated with the ENL reference corpora. The most striking divergence, however, was observed with the semantic domain of ‘communication’ in the Conversation register (see 4.3.4.3). Indeed, verbs such ARGUE, JOKE, TALK, TRY and SAY were consistently found to be less strongly associated with the progressive in Textbook Conversation across all analyses.

Finally, we asked whether the differences noted in the use of progressives across Textbook English and the ENL reference corpora made pedagogical sense, or whether they might result in learners being unfamiliar with frequent, and thus arguably useful, progressive constructions. Throughout the discussion of the results, it was made clear that many of the differences, especially in terms of tense form and time reference distributions, are probably pedagogically well-founded. Similarly, it may be argued that since question syntax poses a lot of problems for EFL learners, an over-representation of this form in the progressive might also serve a sound pedagogical purpose. In contrast, the notable under-representation of many typical discourse phrasemes featuring progressives seems more problematic. It is likely to result in learners sounding unidiomatic and, for some, may even contribute to their struggle to engage in natural, spontaneous conversation with proficient speakers of English. Similarly, learners’ lack of exposure to central functions of the progressive such as repeatedness or gradual change may lead to genuine difficulties in listening and reading comprehension. Some of the differences observed in the lexical verbs associated with the progressive appear to stem from textbook authors’ avoidance of many idiomatic light verb constructions (e.g., HAVE *an* argument/chat/conversation), which frequently call for the progressive in both conversation and fictional writing. The next chapter homes in on textbook authors’ apparent (see 2.2.1.2) underuse of idiomatic collocations, including light verb constructions and phrasal verbs, in secondary school EFL textbooks as it examines the use of MAKE in Textbook Conversation and Textbook Fiction.
# 5 Making sense of MAKE in Textbook English

> Hey look, some words with real meaning on a shopping street! The sign
> makes you stop and think, doesn't it? It reads like a poem.
> Who needs real meaning on a shopping street?! I'm more into songs anyway.
>
> <TEC: Green Line New 3>

This case-study chapter<fnref n="30" /> focuses on the high-frequency verb MAKE, exploring its semantic and phraseological representation in the Conversation and Fiction subcorpora of the TEC as compared to the Spoken BNC2014 and the Youth Fiction corpora. Presentations of MAKE in Textbook English are additionally compared to the results of relevant learner English corpus studies.

## 5.1 Rationale and aims

MAKE is typically one of the first verbs to be taught in EFL lexical syllabi. Its monosyllabic form and (often mistakenly) assumed equivalence with equally frequent verbs in other Indo-European languages such as *faire*, *machen* and *hacer*, has contributed to MAKE being commonly perceived as an “easy verb” for English learners to master (see, e.g., Lennon 1996; Gouverneur 2008a: 223; Liu & Shaw 2001: 188). However, it is well known that with high frequency comes polysemy and, in the case of MAKE, this involves delexical, phrasal, prepositional and other idiomatic uses. In addition, just like *faire*, *machen* and *hacer*, MAKE enters into many restricted collocations (as defined by Cowie 1998) that often cannot be translated by simple one-to-one equivalences (e.g., Dirven & Radden 1977), hence generating a high potential for L1 interference in L2 learners’ production, as illustrated in (128)–(131) from the French and Spanish L1 subcorpora of the Open Cambridge Learner Corpus (Cambridge University Press 2017, available on Sketch Engine).

(128) It has become impossible nowadays to make some research [~ faire des recherches] without the Internet. <OCLP: L1_FR, 359, C2>

<footnote n="30">³⁰ Preliminary results of the analyses from this chapter were presented at the IVACS 2019 symposium in Dortmund. Parts of this chapter have been published as: Le Foll, Elen (2022). *MAKING tea and mistakes*: The Functions of MAKE in Spoken English and Textbook Dialogues. In Zihan Yin & Elaine Vine (eds.), *Multifunctionality in English: Corpora, Language and Academic Literacy Pedagogy*, 157–178. Oxon; New York: Routledge and Le Foll, Elen (2023). Textbooks and Corpus Linguistics: the case of causative constructions. In Kieran Harrington & Patricia Ronan (eds.), *Demystifying Corpus Linguistics for English Language Teaching*. Palgrave MacMillan.</footnote>
(129) Step by step, you decide to work all of the night, and at the end you
forget your body and begin to die. It **has no sense** [~ avoir du sens →
make sense]; you must respect your own life. <OCLC: 5411, B1>

(130) I have been waiting for the T.V. repair main [sic] on Wednesday A.M. but he didn't come at all. I phoned the firm and they have **took a new appointment** [~ prendre un rendez-vous → make an appointment] in two weeks time. <OCLC: L1_FR, 4440, B1>

(131) It was dark, the weather was quite bad, the wind was hitting the windows
and I was thinking on what day I had chosen to **make a party** [~ hacer
una fiesta]. <OCLC: L1_FR, 11372, B1>

(132) Sometimes I forced myself to **make some work** [~ hacer algunos trabajos] I had left behind and suddenly I found myself as a selfish mother that cares more about work than about her son. <OCLC: L1_ES, 7664, C2>

Moreover, MAKE is often perceived as the most prototypical causative verb (Altenberg 2002: 99). Bearing this in mind, Gilquin (2016a: 236) hypothesises that MAKE may “serve as a pathbreaking verb in instruction, that is, a verb that is used to introduce the general characteristics of the construction, before the construction is extended to other verbs”. Indeed, causative constructions with MAKE (e.g., *it makes you stop and think*) are both highly frequent and of high communicative value; however, they also form complex syntactic patterns which often prove problematic for learners of English (e.g., Altenberg & Granger 2001; Gilquin 2012; 2016a; Liu & Shaw 2001; Wong 1983). Such syntactic errors have long been recognised in mainstream English Language Teaching (ELT) literature (e.g., error note on MAKE in the *Longman Dictionary of Common Errors*, Turton & Heaton 1996, cited in Gilquin 2012: 42). These various potential pitfalls have inspired a plethora of corpus-based studies on English learners’ unidiomatic use of MAKE (e.g., Altenberg & Granger 2001; Gilquin 2016a; Liu & Shaw 2001; Nesselhauf 2004).

A selection of the many studies exploring the use of MAKE in learner Englishes is surveyed in 5.1.1. In contrast to this wealth of learner English studies, only one study (Gouverneur 2008b) has, to the present author's best knowledge thus far, delved into the representation of MAKE in what is arguably EFL learners' first and main source of language input: their textbooks. Key findings of this precursor to the present study are summarised in 5.1.2. In light of those conclusions, as well as the findings of past learner English studies (see 5.1.1), the present chapter seeks to explore the following research questions:

1. Is the verb MAKE significantly over- or underrepresented in the conversation and fiction texts of contemporary secondary school EFL textbooks as compared to target language reference corpora of similar registers?

2. Which semantic functions does MAKE in Textbook English typically fulfil? Are these functions representative of the most common meanings of MAKE?
3. Which collocates and semantic fields are typically associated with MAKE in textbook conversation and fiction texts? How does this compare the language of similar registers that learners are likely to encounter outside the EFL classroom?

4. To what extent are known L2 English learners' unidiomatic usage, underuse or overuse of the verb MAKE (see 5.1.1) potentially traceable to a lack of adequate linguistic input in EFL textbooks?

5. Which of the differences noted in the representations of MAKE across Textbook English and the target language reference corpora may be pedagogically well-founded? Are any susceptible of depriving learners of valuable exposure to frequent forms with particularly useful communicative functions?

The methodology to do so is outlined in 5.2. The results of this comparative, Textbook English vs. target 'real-life' language, are analysed and discussed in 5.3. In the interest of clarity, this section is subdivided according to the broad semantic categories identified and compared in 5.3.2: MAKE in the sense of 'produce' (5.3.3), as a delexical verb (5.3.4) and as part of phrasal verbs<fnref n="31" /> (5.3.5). Section 5.3.6 examines the syntactic and phraseological patterns of causative MAKE constructions in EFL textbooks. The chapter concludes with an outline of the key pedagogical implications of the present findings (5.4).

### 5.1.1 MAKE in Learner English

Altenberg and Granger (2001: 176–178) report that French learners of English underuse MAKE in their essay writing and, in particular, significantly underuse delexical and causative uses of the verb as compared to ENL students. Further, both French and Swedish learners appear to underuse MAKE collocations with ‘speech’ or ‘verbal communication’ noun objects (e.g., *argument*, *claim*, *point*, *statement*).

Nesselhauf (2004) focused on German L1 speakers' use of light verb constructions (referred to as 'delexical' verbs in this chapter, see 5.2) with the verbs HAVE, TAKE, GIVE and MAKE. Since they are based on a relatively small learner corpus (a preliminary version of ICLE-German subcorpus with some 150,000 words; Granger et al. 2009), the results must be interpreted with some caution; however, they nevertheless suggest that, out of these four high frequency verbs, German learners are most liable to making errors in support verb constructions featuring MAKE.

In addition, a number of studies (Altenberg & Granger 2001; Gilquin 2012; 2016a; Liu & Shaw 2001; Wong 1983) have pointed to English learners' tendency to frequently make infelicitous syntactic and lexical choices when producing causative constructions with MAKE. For instance, Gilquin (2016a) compared the lexical choices of ENL, ESL and EFL writers for the non-finite verb slot in causative constructions.

<footnote n="31">³¹ As is most frequently the case in ELT contexts, the term ‘phrasal verbs’ here refers to both phrasal and prepositional verbs. Thus, the term encompasses *verb + adverb* (*MAKE off*), *verb + preposition* (*MAKE for*), and *verb + adverb and preposition* (*MAKE away with*) constructions.</footnote>
Using distinctive collexeme analysis (see 4.2.3.2), she was able to identify and quantify
the lexical idiosyncrasies observed in MAKE causatives in ESL and EFL student
writing. One important finding is that, whilst both types of learners overuse a small
number of verbs (foremost, the statives BE and BECOME) and underuse others (in
particular, SEEM and APPEAR) in the non-finite verb slot of [X MAKE Y V<sub>inf</sub>]
constructions, EFL learners overuse a number of other verbs compared to ENL
students, including BELIEVE, GO AROUND and FEEL (Gilquin 2016a: 243). Gilquin
(2012; 2016a) identifies three possible causes for learners' unidiomatic use of
periphrastic causative constructions: a) a lack of register awareness, b) negative L1
transfer and c) the inadequacy of teaching materials. This final potential factor, also
identified by Altenberg & Granger (2001: 184) in the context of EFL learners and
causative constructions with MAKE, will be at the heart of this case study on the
representations of MAKE in school EFL textbooks.

5.1.2 MAKE in Textbook English

As mentioned in the introduction to this chapter, Gouverneur (2008b) investigated
the selection and presentation of the phraseological patterns of MAKE (and TAKE) in
Textbook English. The analysis was based on a subset of the TeMa corpus (Meunier
& Gouverneur 2009), consisting of six intermediate and advanced English for general
purposes (EGP) textbooks. It focused exclusively on the exercises included in these
textbooks and their accompanying materials, i.e., the student workbooks. The
detailed pedagogical annotation of the vocabulary exercises in the TeMa corpus
singles it out from other textbook corpora: over 80 tags were manually applied to the
exercises to encode the type of learning activity the learners engage in (e.g., match
words and definitions, complete sentences) and the way each target lexical item is to
be completed within the exercises (e.g., select from a word box above task, cross out
incorrect answer within the text, retrieve from the mental lexicon) (Meunier &
Gouverneur 2009: 189–195). From this vocabulary exercise subcorpus, all 298
occurrences of MAKE were automatically retrieved and manually coded for meaning
and phraseological patterning. Gouverneur’s fine-grained analysis then focuses on
verb-noun restricted collocations with MAKE. Phrasal verbs were not included in this
study.

According to Gouverneur's classifications (2008b: 233), by far the most frequent use
of MAKE in the TeMa exercise subcorpus is in restricted collocations, which, in her
study, included delexical uses and the senses 'do/perform' and 'earn'. At first, this
finding seems promising, given that such collocations are known to be considerably
more problematic for learners of English than MAKE in its prototypical sense of
'produce' (e.g., Altenberg & Granger 2001: 189). However, whilst intermediate
textbooks often place direct emphasis on these phraseological patterns, such explicit
treatment of these collocations is much rarer in the advanced textbooks of the TeMa
corpus (2008b: 234–235). Furthermore, intermediate-level exercises more frequently
focus on the verb slot in these constructions than the tasks from the advanced
textbooks, which tend to focus on the collocate object slot. Learner corpus studies,
however, have shown that even advanced L2 users more often struggle with the choice
of verb (e.g., Nesselhauf 2004: 71) than with the choice of object noun. Finally, very
few of the phraseological patterns with MAKE observed in the TeMa subcorpus were
common to all textbooks of the same level (15% across the three intermediate
textbooks and 7% across the advanced textbooks), pointing to a lack of systematicity
in materials authors' choices.

## 5.2 Methodology

This section describes the methodology designed to compare the representations of MAKE in Textbook Conversation and Textbook Fiction with naturally occurring ENL data. First, all instances of MAKE as a verb were retrieved from the Fiction and Conversation subcorpora of the TEC. In total, 674 occurrences of MAKE were extracted from the Conversation subcorpus, and 392 from the smaller Textbook Fiction subcorpus. In addition, the same number of occurrences were randomly sampled from the Spoken BNC2014 (n = 674), as well as from the Youth Fiction Corpus (n = 392). Subsequently, the concordance lines corresponding to these 2,132 occurrences of the verb MAKE were coded for a) meaning, b) main collocate lexemes, and c) semantic field attributed to the collocates.

In a preliminary round of coding for meaning, the semantic categories proposed by Altenberg & Granger (2001) and Gouverneur (2008b) (themselves based on well-known learner dictionaries) were adopted. However, these categorisation schemes proved to be rather susceptible to subjective judgements and there was too little information in these publications to follow exactly the same coding procedure and thus obtain genuinely comparable data. Having compared alternatives, it was ultimately decided to apply the meaning categories derived from the *Valency Dictionary of English* (hereafter VDE) (Herbst, Heath & Roe 2013: 513–517) whose categories are more thoroughly delimited and include exemplifications of the most frequent phraseological patterns (see Table 33). Nonetheless, in some cases, the distinction between the first two categories of the VDE’s MAKE entry, ‘Ai produce’ and ‘Aii delexical’ (see Table 33), remained problematic. Indeed, over the course of the coding process, it became clear that the term ‘delexical’, though highly frequent in both the English linguistics and ELT literature, is rarely defined; linguists seemingly operationalise the term in a myriad of ways. In an attempt to remedy this, the present study adopted the definition and examples provided in the *Collins Cobuild English Grammar* (Sinclair et al. 1990: 147–151):

[Delexical verbs are] very common verbs which are used with nouns as their object to indicate simply that someone performs an action, not that someone affects or creates something. These words have very little meaning when they are used in this way [...] In many cases, there is a verb which has a similar meaning to the meaning of the delexical structure. [...]
When you use the word as a noun in a delexical structure, you are naming an event, something which is complete. This structure often seems to be preferred to a structure in which the verb has greater prominence. Note that the verb which corresponds to the delexical structure is often intransitive (Sinclair et al. 1990: 147).

<table><caption>Table 33: Semantic categories of MAKE according to the VDE (Herbst, Heath & Roe 2013) illustrated with sentences from the TEC</caption><thead><tr><td>Semantic category</td><td>Meaning</td><td>Example sentence</td></tr></thead><tbody><tr><td>Ai: Produce</td><td>Construct or create</td><td>I want to <b>make a carrot cake</b>.<br/>&lt;TEC: Solutions Elementary&gt;</td></tr><tr><td>Aii: Delexical produce</td><td>Performing an action</td><td>People always <b>make the mistake</b> of thinking that<br/>[...]. &lt;TEC: New Missions 2<sup>de</sup>&gt;</td></tr><tr><td>B: Earn</td><td>Money</td><td>But I've <b>made enough money</b> to buy them a house<br/>[...]. &lt;TEC: Green Line 2&gt;</td></tr><tr><td>C: Achieve</td><td>Typically used with<br/>destinations or<br/>targets to be reached</td><td>There's a lot of pressure and stress, but I've<br/><b>made it</b> [...]. &lt;TEC: Green Line New 5&gt;</td></tr><tr><td>D: Be/become</td><td>Particularly suited<br/>for a role or function</td><td>I think women <b>make better poets</b> than men.<br/>&lt;TEC: Solutions Intermediate&gt;</td></tr><tr><td>E: Cause</td><td>Cause</td><td>[T]hey don't <b>make me wear</b> things that I don't<br/>like. &lt;TEC: Join the Team 3<sup>e</sup>&gt;</td></tr><tr><td>F: Ensure</td><td>MAKE sure/certain</td><td>So the teacher can <b>make sure</b> the student really<br/>understands. &lt;TEC: Solutions Intermediate&gt;</td></tr><tr><td>I: Idioms</td><td>Other idiomatic<br/>phrases</td><td>Maybe I can <b>make new friends</b> there.<br/>&lt;TEC: Access G 1&gt;</td></tr><tr><td>P: Phrasal verbs</td><td>MAKE followed by<br/>one or more<br/>prepositions/particles</td><td>[...]. I always thought they <b>made that up</b> to scare<br/>us.<br/>&lt;TEC: Green Line 5&gt;</td></tr></tbody></table>

In the manual coding scheme of the MAKE concordance lines, the variable ‘collocate lexeme’ was designed to capture the object, most frequently a noun, in MAKE occurrences of the semantic categories A-D (e.g., *cake*, *mistake*, *money*, *it* and *poets* for the example sentences listed in Table 33). It was also used to record the non-finite verbs in the periphrastic causative constructions of category E (e.g., *wear*) and the relevant prepositions/particles for category P (e.g., *up*). The collocate lexemes were automatically lemmatised using the procedure already described in 4.2.2.1.

In addition, the semantic fields attributed to the ‘produce’ collocates were initially automatically tagged with the USAS Wmatrix3 English web tagger (Rayson 2018). To begin with, only the 21 upper-level major discourse fields of the tagset were considered (Archer, Wilson & Rayson 2002). These category codes were manually verified, corrected and refined. To better capture the variance of the data, the following adjustments were made to the original Xmatrix classification scheme: owing to the large number of MAKE collocates belonging to category F (‘Food’), this category was subdivided according to the tagset’s subcategories F1 (‘Food’) and F2 (‘Drink’).
Similarly, category B ('the body and the individual') was subdivided into 'Body' (e.g., *fingerprint, hair*) and 'Clothing & Accessories' (e.g., *bracelet, costume*). Subsequently, the categories 'Body', L ('Life and living things') and W ('World and environment') were combined to form a new 'World, Life & Body' category. Due to very low figures and partially overlapping terms, categories I ('Money and commerce in industry') and Y ('Science and technology') were merged into one 'Industry & Technology' category, whilst E ('Emotion'), G ('Government and public'), N ('Numbers and measurement') and P ('Education') were merged into a 'General & abstract terms' category.

The following section (5.3) presents the results of the analyses of the use of MAKE in Textbook English conducted on the basis of the 2,132 annotated concordance lines. It provides an overview of the frequencies of MAKE across all the register subcorpora of the TEC (5.3.1), before focusing on MAKE in the Conversation and Fiction subcorpora. The final results subsection, 5.3.6, explores the syntactic and phraseological patterns of MAKE as a causative verb across all textbook registers.

## 5.3 Results and discussion

### 5.3.1 MAKE in the Textbook English Corpus (TEC)

As a verb, MAKE is typically first introduced within the very first pages of beginner textbooks. Across all textbook series and countries of use of the TEC, learners first encounter the verb MAKE in the context of instructions, e.g., (133)–(139). Correspondingly, the strongest object collocates of MAKE in the beginner textbooks in the TEC (level A, see Table 3) are *list, sentence, note* and *dialogue*. Thus, we see that these early exposures to this multifunctional verb already include delexical uses of MAKE, e.g., (138)–(139).

(133) Make groups of three. <TEC: Access G 1>

(134) Practise: Make sentences to describe your house. <TEC: Piece of Cake 6º>

(135) Put the words in the correct order and add do or does to make questions.
<TEC: Achievers A1>

(136) Use the pictures to make conversations. <TEC: English in Mind Starter>

(137) Make notes while you listen. <TEC: Green Line New 1>

(138) Make a list of the characters. Make a list of Tom's mother's different problems. <TEC: Join the Team 6º>

(139) Look at the posters and make suggestions. <TEC: Hi There 6º>

Across the entire Textbook English Corpus, MAKE is the 9th most frequent verb lemma after BE, DO, HAVE, GO, GET, SAY, USE, and THINK. Moreover, it is the 45th most frequent lemma across all parts-of-speech with a relative frequency of occurrence
across all textbook registers of 1,407 pmw<fnref n="32" />. Per 10,000 verbs, MAKE has a relative frequency of 144.2 (SD = 56.81). That said, Table 34 shows that its distribution across the different registers of the TEC is heavily skewed towards instructional language, which, it is worth recalling, accounts for just over a fifth of the total English word count in the TEC (see Fig. 2).

<table><caption>Table 34: Frequency of the verb lemma MAKE in the TEC</caption><thead><tr><th>TEC subcorpus</th><th>MAKE<br>(n)</th><th>MAKE<br>(per million words)</th><th>MAKE<br>(per 10,000 verbs)</th></tr></thead><tbody><tr><td>Conversation</td><td>699</td><td>1,368</td><td>87</td></tr><tr><td>Fiction</td><td>395</td><td>1,556</td><td>93</td></tr><tr><td>Individual words/sentences</td><td>1,776</td><td>1,943</td><td>128</td></tr><tr><td>Informative texts</td><td>713</td><td>2,354</td><td>162</td></tr><tr><td>Instructional texts</td><td>2,310</td><td>3,902</td><td>259</td></tr><tr><td>Other texts</td><td>29</td><td>2,015</td><td>140</td></tr><tr><td>Personal correspondence</td><td>115</td><td>1,714</td><td>103</td></tr><tr><td>Poetry & rhyme</td><td>82</td><td>3,131</td><td>182</td></tr></tbody></table>

The following fine-grained analyses will home in on the two textbook registers with the lowest relative frequencies of MAKE, namely Fiction and Conversation. As laid out in 5.2, the representations of MAKE in these two textbook registers are compared to the Youth Fiction corpus and the Spoken BNC2014 respectively.

The first comparison concerns the relative frequencies of MAKE in these two textbook registers as compared to their corresponding ENL reference corpora. The results of this analysis are displayed in Fig. 18. It provides clear evidence that, across both language varieties, the verb MAKE is more frequent in fiction than conversation. However, we note that the relative frequency of MAKE is considerably lower in Textbook Fiction. By contrast, MAKE is more frequent in Textbook Conversation than it appears to be in naturally occurring ENL conversation. Indeed, Fig. 18 shows that the difference in relative frequencies is considerably less pronounced between the textbook registers than it is between each ENL register, a finding that echoes that observed with progressives (see 4.4). Chi-square tests (with Yates' continuity correction) applied to the observed and expected raw frequencies indicate that these differences between each textbook register and their corresponding reference corpora are significant; however, the effect sizes are minimal, suggesting that these differences are unlikely to be pedagogically relevant (Conversation: $\chi^2(1) = 20.22, p < 0.001$, $\varphi = 0.003$; Fiction: $\chi^2(1) = 17.26, p < 0.001$, $\varphi = 0.003$).

<footnote n="32">³² Calculated using Sketch Engine by searching for the lemma MAKE with a verb POS in the TEC.</footnote>
<figure><img src="image_21.png" /><figcaption>Fig. 18: Relative frequencies of the verb lemma MAKE in Textbook and Reference subcorpora</figcaption></figure>

### 5.3.2 Semantics of MAKE in Textbook Conversation and Fiction

High-frequency verbs such as MAKE are not only highly polysemous, but they also enter into a vast range of (semi-)restricted lexico-grammatical constructions. This section explores the quantitative differences in the proportional use of these different meanings and constructions in the same two Textbook English registers as compared to similar registers in naturally occurring ENL speech and writing.

Fig. 19 visualises the different proportions of meanings of MAKE in Textbook Conversation and in the Spoken BNC2014 sample. Statistical differences between the proportions of each category were tested using Fisher's exact test for count data. On all the plots featured in this chapter, the resulting *p*-values are plotted using the following key: *p* < 0.001 (***), 0.001–0.01 (**), 0.01–0.05 (*) , 0.05–0.1 (~).

<figure><img src="image_22.png" /><figcaption>Fig. 19: Comparison of the distribution of MAKE meanings in Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>
Causative uses of MAKE represent the most frequent semantic category in the Spoken BNC2014 sample. The proportion of ‘causative’ MAKE occurrences in Textbook Conversation, however, is significantly lower (p = .01, OR = 1.36, 95% CI 1.07–1.74). Causative uses of MAKE will be further explored in 0. The next two most frequent categories, the ‘produce’ sense and delexical uses, are similarly distributed across the two conversation corpora. The significantly higher proportion of idioms featuring MAKE in Textbook Conversation (p = .004, OR = 0.48, 95% CI 0.28–0.81) is primarily due to the very high frequency of the idiomatic phrase *to MAKE friends* (see Table 35). This is clearly a strong topical focus in many of the teenage dialogues written for school textbooks, e.g., (140)–(141).

<table><caption>Table 35: Distribution of the most frequent idioms featuring MAKE in Textbook Conversation and the Spoken BNC2014 sample (absolute frequencies)</caption><thead><tr><th>Idiomatic phrase</th><th>Textbook Conversation</th><th>Spoken BNC2014 sample</th></tr></thead><tbody><tr><td>MAKE friends</td><td>25</td><td>5</td></tr><tr><td>MAKE the best/most of sth.</td><td>4</td><td>2</td></tr><tr><td>MAKE faces</td><td>4</td><td>4</td></tr><tr><td>MAKE fun of sth./sb.</td><td>3</td><td>1</td></tr><tr><td>Practice makes perfect.</td><td>3</td><td>0</td></tr><tr><td>MAKE up YOUR mind</td><td>5</td><td>3</td></tr></tbody></table>

(140) Dave: That's nice for people on holiday - but I'll be in a new school, and there'll be nobody I know. It'll be horrible. And I'm sure Sid will hate it too.
Jay: Don't worry, you'll **make lots of new friends**. <TEC: Green Line New 2>

(141) Finally, although school trips are fun and help students to relax and **make friends**, sport is even better for doing these things. All in all, I take the view that schools should spend more on sport than on music or school trips. <TEC: Solutions Pre-intermediate>

Fig. 20 compares the distributions of MAKE meanings in Textbook Fiction and a sample of the Youth Fiction Corpus. The most striking difference is found in the significant underrepresentation of delexical uses of make in Textbook Fiction as compared to the Reference Youth Fiction corpus (p = .04, OR = 1.42, 95% CI 1.01–2.01). A more fine-grained analysis of delexical uses of MAKE in Textbook English will be presented in 5.3.4.

Moreover, both textbook registers under study, Conversation and Fiction, feature significantly fewer phrasal verbs with MAKE than their corresponding ENL reference corpora (see Fig. 19 and Fig. 20). Since phrasal verbs are known to represent a
stumbling block for many learners of English as an L2, this is pedagogically relevant finding that will be further elaborated on in 5.3.5.

<figure><img src="image_23.png" /><figcaption>Fig. 20: Comparison of the distribution of MAKE meanings in Textbook Fiction and Youth Fiction</figcaption></figure>

Quantitatively, the semantic category ‘achieve’ appears to be overrepresented in Textbook Fiction as compared to the Youth Fiction reference corpus. This is primarily due to the fact that, in Textbook English, the ‘achieve’ category is largely dominated by the phrase *to MAKE it*, e.g., (142)–(143).

(142) And we did have enemies – both the white soldiers and the Omaha warriors, who were always trying to capture Kaw boys and girls undergoing their endurance test. It was an exciting time." "What happened if you couldn't make it?" Roger asked. <TEC: Green Line 4>

(143) "They'll never make it," he whispered. "Teresa has a bad leg and the kids... they can't outrun those things. <TEC: Join the Team 3e>

This phrase is found to occur 17 times in Textbook Fiction, as opposed to just three times in the Youth Fiction sample. By contrast, a qualitative analysis of the corresponding concordance lines reveals that, in the Youth Fiction sample, the ‘achieve’ category includes a much broader range of intransitive motion constructions (see Goldberg 1995: 3; Rohde 2001; Stefanowitsch 2013), e.g., (144)–(146). These constructions, typical of narrative prose, and syntactically relatively complex because they usually contain one or more adverbs or prepositions, are, however, conspicuously absent from the Textbook Fiction subcorpus. Extract (146) exemplifies the *way*-construction (see Goldberg 1995: chap. 9; 1996; Israel 1996; Traugott & Trousdale 2013), in which MAKE has a “privileged status” as its strongest collexeme, accounting for some 20% of all occurrences of the construction, and as the first verb used in the *way*-construction before it was extended to other lexical verbs (Goldberg 1996: 39). This construction is largely absent from Textbook Fiction.
(144) [...] Jack came out of the barn and made straight for me.
<Youth Fiction: Douglas 1979: The Hitch Hikers Guide to Galaxy>

(145) She and the bird started to make off towards my ship.
<Youth Fiction: Lewis 1952: The Chronicles of Narnia: The Voyage of the
Dawn Treader>

(146) Then he made his way down the stairs and into the locker room.
<Youth Fiction: Blume 1974: Blobber>

### 5.3.3 MAKE in the prototypical ‘produce’ sense

Since the prototypical ‘produce’ sense of MAKE (sense Ai in the VDE) accounts for a considerable proportion of the occurrences of MAKE in Textbook English, this section delves further into the semantic fields to which the object noun collocates of MAKE in this primary ‘produce’ sense can be attributed. Fig. 21 first compares the distributions of the semantic fields attributed to the noun collocates of MAKE in the ‘produce’ sense in Textbook Conversation and the Spoken BNC2014. The semantic categories are ordered from least to most similarly distributed.

<figure><img src="image_24.png" /><figcaption>Fig. 21: Distribution of the semantic fields attributed to the noun collocates of MAKE in the (Ai) ‘produce’ sense in Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

In order to examine the actual lexical co-occurrences at work behind these numbers, Fig. 22 displays the most frequent object collocates. As ‘produce’ MAKE collocations are more frequent in Textbook Conversation than in the Spoken BNC2014 sample, relative frequencies as a percentage of all the ‘produce’ MAKE occurrences within each dataset are plotted and since the frequencies of the collocates follow Zipfian distributions, they have been log-transformed for better readability. Thus, the collocates on or close to the diagonal dotted line are more or less equally represented in the two sets of ‘produce’ MAKES. Collocates in the upper segment are more strongly
represented in the Spoken BNC2014 sample than in Textbook Conversation, and the opposite is true of collocates displayed in the lower segment of the plot. The font colours of the collocate lemmas correspond to their attributed semantic field categories (see 5.2).

<figure><img src="image_25.png" /><figcaption>Fig. 22: Frequent collocate lemmas of MAKE in the (Ai) ‘produce’ sense in Textbook Conversation and Spoken BNC2014 sample</figcaption></figure>

Fig. 21 shows that MAKE collocations pertaining to sports, entertainment and travel are far more frequent in textbook dialogues than in the Spoken BNC2014 sample ($p < .001$, OR = 0.29, 95% CI 0.14–0.58), whilst Fig. 22 shows that this semantic category is largely dominated by making *films*. The making of *records*, *movies* and *videos* is also more frequently the subject of discussions in textbook dialogues than in the reference data. Indeed, these activities are frequently discussed by the teenage characters featured in textbook dialogues, e.g., (147)–(148). Moreover, several textbook series encourage learners to produce short videos as part of their English lessons and some of the dialogues featuring these collocations aim to model the discussions that students could have when working on these video-making activities, e.g., (148).

(147) Sam: Well, do you have any other ideas? Justin: Sorry, but I want to make a video about Plymouth for my dad. <TEC: Access G 1>

(148) Hey! I'm making a little film to introduce our school to new students. Can I ask you some questions? <TEC: Green Line New 4>
By contrast, the most common semantic field associated with ‘produce’ MAKE collocations in the reference Spoken BNC2014 sample is ‘Food’, which is significantly more frequent than in Textbook Conversation (p = .04, OR = 1.59, 95% CI 1.01–2.49). The informal context in which the vast majority of the conversations were recorded for the Spoken BNC2014 is likely to be a major factor contributing to the high proportion of MAKE collocates relating to food and drink. Indeed, it was recommended that contributors to the Spoken BNC2014 record their conversations in quiet locations, such as at home or in cafés (Love, Hawtin & Hardie 2018: 4). Food stuffs that are frequently featured in such collocations include *chips*, *soups*, *bread*, *curries*, *mince pies*, *gravy*, *cake* and *sandwiches*, e.g., (149)–(150). The latter two are also frequently represented in Textbook Conversation.

(149) well we were gonna get a takeaway but then we just got I made a curry
**and stuff**
ah nice
yeah
I love a good curry
it was good actually didn't eat for ages though it took for ages to make
<BNC2014: SLDD>

The *Food* collocates found in the Spoken BNC2014 provide a fascinating window into
the multicultural nature of British culinary habits; thus, the sample of 673 MAKE
occurrences examined in this study includes MAKE collocations with foods as varied
as *bacon baps*, *burgers*, *bhajis*, *chutneys*, *curries*, *fudge*, *mash*, *matzah*, *mince pies*,
*toad-in-the-hole* and *Yorkshire puddings*! Confirming the British stereotype, the most
frequent ‘Drink’ collocate is *tea*. However, it should be noted that not all MAKE + *tea*
collocations refer to the brewing of Brits’ favourite hot drink: in many cases, MAKE
*tea* refers to preparing an (early) evening meal, e.g., (150). This common, regional
use of the collocation (Smith 2018) is not featured in any of the textbook dialogues
of the TEC. Conversely, MAKE + *dinner* is proportionally more frequent in Textbook
Conversation than in the Spoken BNC2014, e.g., (151). Note that the use of the
definite article in excerpt (151) is unusual for habitual actions: this phraseological
pattern is found just once out of 38 occurrences of MAKE + *dinner* across the full
Spoken BNC2014.

(150) I don't feel like cooking is my job and I have to do it
I know occasionally **make sandwiches** for me but I couldn't stand it if I
was like **made sandwiches** I just think that would be horrible I just
don't know it's weird or like where's my tea
yeah I've got your dinner on the table for when you come in be awful
wouldn't it what I don't know I like you like **making tea** and tidying up
and stuff but I think I feel guilty <BNC2014: S5YQ>

(151) I do my best to help with the housework, but it's difficult to find the time. I tidy my bedroom once a week and I sometimes take the rubbish out or help mum to **make the dinner**. <TEC: Solutions intermediate>
The most frequent food-related collocations in Textbook Conversation is MAKE *breakfast* (152). For this phraseological pattern, the absence of a definite article is accurately portrayed in the pedagogical dialogues. However, whilst also observed in the Spoken BNC2014, by far the most frequent verb collocate for *breakfast* in natural conversation is HAVE (153), rather than MAKE.

(152) Boy 1: Yeah, good idea: I can **make breakfast** for her! I never do that, ever. But now we've got a new problem: How do you **make breakfast**?<br/><small>&lt;TEC: Green Line New 1&gt;</small>

(153) well we could do that tomorrow if you want for lunch<br/>er I was planning on **having breakfast** for lunch tomorrow<br/>Fair enough we could buy some brunch stuff if you want or just **have**<br/>**breakfast**<br/>just **have breakfast** really I like brunch occasionally but not as a<br/>regular thing <BNC2014: SCNN>

Fig. 19 also reveals that MAKE collocations from the realm of ‘Clothing & Accessories’ are somewhat more frequent in textbook dialogues than in the reference conversational data ($p$ = .02, OR = 0.30, 95% CI 0.08–0.86). This is likely a topic effect: items of clothing are often taught early on in EFL syllabi and this vocabulary is frequently recycled in textbook dialogues, e.g., (154)–(156). Many of these collocations are in the passive form, where either the person who designed or produced the items are the focus on the utterances (155), or the material out of which they are made (156).

(154) Your clothes look cool too. Did you use to spend a lot of money on them?
I didn't use to have much money. My mother **made some of them**. And I used
to share clothes with my brother. <TEC: Solutions intermediate>

(155) MEGAN: Did you watch the royal wedding? Wow, what a ceremony!
ALEX: Uhuh. I watched it for a while. Kate was beautiful, wasn't she?
MEGAN: Yes, she was. Her dress was just gorgeous. **It was made** by Sarah
Burton, an English designer. Kate is such a fashion icon!
<TEC: Piece of cake 4>e>

(156) Do you like my T-shirt? **It was made** from recycled plastic.
<TEC: Achievers B1>

As the interlocutors of the Spoken BNC2014 share knowledge and a spatial environment that also allows them to communicate non-verbally, the corpus features many ‘produce’ MAKE collocations where the collocate is not explicitly mentioned (157). Thus, the collocate lemma tag [*/food*] in Fig. 21 and Fig. 22 refers to MAKE collocations that, whilst evidently referring to MAKING food of some kind, do not mention a specific collocate. In addition, vague language is much more frequent in natural conversation (158), than in the textbook dialogues which, on the contrary, are frequently very precise in their descriptions (159). As a result, textbook dialogues are characterised by high lexical diversity and many more attributive adjectives than the transcripts of authentic conversations.
<figure><img src="image_26.png" /></figure>

As already observed in Chapter 4, the differences between Textbook Fiction and the Youth Fiction reference corpus are not as pronounced as in Textbook Conversation and its corresponding reference corpus. Thus, Fig. 23 reveals just two significant differences in the distribution of the semantic fields attributed to ‘produce’ MAKE collocations. Note also that, although the lines plotted in Fig. 23 are longer than in Fig. 21., the range of values on the y-axis is also considerably smaller.

<figure><img src="image_27.png" /><figcaption>Fig. 23: Differences in semantic fields attributed to collocates of MAKE in the (Ai) ‘produce’ sense.</figcaption></figure>

The most frequent lexical instantiations of these ‘produce’ collocations cannot be plotted, simply because the vast majority of collocate lemmas only occur once in each sample; thus, hapax legomena make up 95% of ‘produce’ MAKE collocates (n = 77) in the Youth Fiction sample and 75% of these collocates in Textbook Fiction (n = 48). Given the limited vocabulary EFL secondary school pupils are expected to have at this early stage of L2 acquisition, it is not surprising to find higher lexical diversity in novels targeted at English-speaking teenagers compared to the narrative writing of EFL textbooks.
Fig. 23 shows that the proportion of ‘produce’ MAKE collocates from the semantic field of ‘Buildings & House’ is significantly higher in Youth Fiction than in the corresponding textbook register (p = .004, OR = 5.78, 95% CI 1.53–32.57). Collocate lemmas in this semantic field include *building, carpet, chair, door, path, [zebra] pen, roof, room, settlement, shelter, steeple* and *wall*. By contrast, Fig. 23 suggests that the semantic field of *Food* may be marginally overrepresented in Textbook Fiction (p = .07, OR = .42, 95% CI 0.16–1.06). Collocates in this category include *breakfast, lunch* and *dinner*, as well as specific foodstuffs such as *pancakes, toast, soup* and *sandwiches*.

### 5.3.4 MAKE as a delexical verb

Quantitatively, Fig. 19 in 5.3.2 showed that the proportion of MAKE occurrences entering delexical collocations in Textbook Conversation is not significantly different from that observed in the Spoken BNC2014 sample (p = 0.14, OR = 0.82, 95% CI 0.63–1.07). However, a closer comparison of the specific collocates of delexical occurrences of MAKE in the dialogues and audio/video transcripts of the Textbook English Corpus with those found in the Spoken BNC2014 reveals some noteworthy patterns. Fig. 24 displays the relative frequencies of the noun object collocates as a percentage of all the delexical MAKE occurrences within each sample. Though also a frequent collocate in the Spoken BNC2014 sample, MAKE + *mistake* stands out as the most frequent delexical collocation in the pedagogical materials, whereas MAKE + *effort* is considerably less frequent in Textbook Conversation than in the reference corpus. In contrast, the most frequent delexical collocation in the reference data is MAKE + *sense*, which, although also featured, is much less frequent in the textbook data. In addition, MAKE + *difference* appears to be slightly less represented in Textbook Conversation. The fact that MAKE + *call* is considerably more frequent in Textbook Conversation is likely an artefact of the Spoken BNC2014, which does not include any telephone conversations, whereas a sizeable proportion of textbook dialogues consists of fictitious telephone conversations.
<figure><img src="image_28.png" /><figcaption>Fig. 24: Most frequent collocates of delexical MAKE in Textbook Conversation and the Spoken BNC2014</figcaption></figure>

Fig. 25 suggests that delexical uses of MAKE in Textbook Fiction writing and the Youth Fiction reference corpus are more similar. Nevertheless, as in the conversation register, MAKE + *mistake* appears to be overrepresented in Textbook Fiction (n = 13) compared to Youth Fiction (n = 4). In addition, MAKE + choice is also considerably more frequent in Textbook Fiction than in the reference data, where MAKE + decision is more frequently observed. The descriptive collocations MAKE + *noise/sound/racket* are among the most common delexical MAKE collocations in both varieties of fictional writing.
<figure><img src="image_29.png" /><figcaption>Fig. 25: Most frequent collocates of delexical MAKE in Textbook Fiction (left) and the Youth Fiction sample (right)</figcaption></figure>

MAKE is known to enter into many delexical structures used to report speech (Sinclair et al. 1990: 150–151). These include MAKE + *arrangement*, *claim*, *comment*, *decision*, *promise*, *protest*, *remark*, *signal* and *suggestion*. In addition, MAKE is also associated with many other speech actions, e.g., *appeal*, *enquiry*, *point* and *speech* (Sinclair et al. 1990: 150–151). In their comparison of the use of MAKE in learner and native student argumentative writing, Altenberg & Granger (2001: 179–180) reported that L2 students significantly underused such ‘speech’ collocates of delexical MAKE; in fact, L1 students employed these collocations more than twice as frequently as L2 students.

Bearing the results from this learner corpus study in mind, it was decided to take a
closer look at delexical MAKE collocates associated with speech/communicative
functions in school EFL textbooks. Fig. 26 shows that the percentages of delexical
MAKE collocates that refer to ‘speech/communication’ actions are lower in the two
textbook registers than in the corresponding reference ENL corpora. However, with
such low numbers involved, these differences do not reach statistical significance.
Nevertheless, it is worth examining potential discrepancies at a quantitative level.
<figure><img src="image_30.png" /><figcaption>Fig. 26 ‘Speech/communication’ delexical MAKE collocations in Textbook Conversation and Textbook Fiction (red) as compared to the reference Spoken BNC2014 and Youth Fiction samples (beige).</figcaption></figure>

First, a number of ‘speech/communication’ delexical MAKE constructions observed in the Spoken BNC2014 are entirely absent from Textbook Conversation. These include *argument*, *assumption*, *comment*, *complaint*, *conversation*, *point* and *row*. Though the raw frequencies within the two samples examined are very low, it is worth noting that three of these (*argument*, *assumption* and *point*, e.g., (160)–(162)) were among those identified as very frequent in ENL essays and underrepresented, or not featured at all, in EFL essays (Altenberg & Granger 2001: 179–180).

(160) yes but it's difficult isn't it? it's like the whale hunting
mm
I suppose whale hunting it's easier to **make an argument** to ban it
because they're endangered species
oh right yeah
whereas bulls aren't endangered <BNC2014: SPG4>

(161) and I and when you meet new people they normally make an **they make an assumption** about you about straightaway it's human nature you make a judgement about someone <BNC2014: S52C>

(162) I **make a valid point** you know I do just <BNC2014: SHXJ>

It might be that textbook authors perceive these delexical constructions as less worthy of teaching or more error-prone than lexical verbs such as *argue*, *assume* and *complain*, e.g., (163)–(165).

(163) You **argued** that we should sign a petition, but I think a flashmob would be more effective. <TEC: Access G 5>

(164) Just because I’m an American teenager, people **assume** I want to wear the same brands as JLo or Sarah Jessica Parker! That’s a cliché!

<TEC: New Bridges 2e>
(165) I was paying by card and I didn't check the amount before I entered my PIN. Anyway, I'm sure the price ticket on the shelf was £10, but she charged me £15. I **complained** and tried to get my money back.
<TEC: Solutions pre-intermediate>

Thus, we may speculate that this underuse of ‘speech/communication’ delexicalize MAKE collocates in EFL learner essays may be partially attributable to textbooks’ underrepresentation of such frequent discourse structures (166)–(167). However, it should be stressed that this claim cannot be empirically backed up with the present, very sparse, data.

(166) um I mean we could book the flights now in fact maybe that's safest you reckon? Do you wanna **make er this decision** now? I just wonder cos that's quite cheap <BNC2014: SU82>

(167) do do you think it's um sort of people are **made aware** of it enough to know? You know what's going on in terms of security settings and things like that or not? <BNC2014: S2PS>

### 5.3.5 Phrasal verbs with MAKE

Though here, too, the absolute figures are low, it was noted in 5.3.2 that the proportional use of MAKE in phrasal verb constructions was significantly lower in both textbook registers under study than in the corresponding ENL reference corpora. Indeed, out of the 674 occurrences of the verb MAKE in the Textbook Conversation subcorpus, just eight were found to form phrasal verbs (thus less than one occurrence per textbook series!) – as opposed to 26 in the randomly collected 674 concordance lines from the Spoken BNC2014 ($p = .003$, OR = 3.33, 95% CI 1.45–8-58). Similarly, out of the 392 MAKE concordances extracted from the Textbook Fiction subcorpus, 13 were coded as phrasal verbs, whereas 29 were identified in the random sample from the Youth Fiction corpus ($p = 0.02$, OR = 2.33, 95% CI 1.15–4.96).

Phrasal verbs with MAKE are usually not introduced until the third or fourth year of English instruction. Indeed, no phrasal verb featuring MAKE was found in any beginner (Level A) textbooks. The majority are in Level D and E textbooks. In total, seven different phrasal verb types are represented in the Textbook Conversation and Fiction subcorpora (see Table 36). However, only two occur more than once, in both cases just twice, in the dialogues and narrative texts of any full textbook series (*MAKE up = comprise* in *Hi There* and *Green Line New*; *MAKE up = invent* in *Green Line*). One French textbook series, *Piece of Cake*, does not feature any phrasal verbs involving MAKE in its spoken language component (nor in its narrative writing but this is less surprising given that the French textbooks contain comparatively few narrative texts, see 3.3.1.4).
<table><caption>Table 36: Absolute frequencies of phrasal verbs with MAKE (out of 674 occurrences of MAKE each for the Conversation samples and 392 for the two Fiction samples)</caption><thead><tr><th rowspan="2">Phrasal verb types</th><th colspan="4">Conversation Fiction</th><th rowspan="2">Examples</th></tr><tr><th>Textbook Conversation</th><th>Spoken BNC2014 sample</th><th>Textbook Fiction</th><th>Youth Fiction sample</th></tr></thead><tbody><tr><td>MAKE it up to sb. (=forgive)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>"I don't know how to <b>make it up to you</b>," he said helplessly <b> Youth Fiction</b>: Ashley 2010: Chocolate Wishes</td></tr><tr><td>BE made of (=capable)</td><td>1</td><td>0</td><td>0</td><td>2</td><td>[...] it makes you stretch your abilities and see what you're really <b>made of</b> <b>TEC</b>: Access 3</td></tr><tr><td>MAKE of sth. (=interpret)</td><td>0</td><td>0</td><td>0</td><td>2</td><td>Oh sir, do read the directions and see what you <b>make of</b> them <b> Youth Fiction</b>: Blyton, 1943, Five Go Adventuring Again</td></tr><tr><td>MAKE out (=perceive)</td><td>0</td><td>1</td><td>4</td><td>10</td><td>I can't quite <b>make it out</b> from my desk <b>TEC</b>: Green Line New 5</td></tr><tr><td>MAKE out (=pet)</td><td>0</td><td>1</td><td>0</td><td>0</td><td>no but it makes you go crazy like in a sexual way so you just start <b>making out</b> with whoever's around <b>BNC2014: SFYP</b></td></tr><tr><td>MAKE out (=present as)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>He had tried to make up his mind whether [...] to <b>make out</b> that he had hurt himself inside very badly <b> Youth Fiction</b>: Blyton, 1943, The Mystery of the Burnt Cottage</td></tr><tr><td>MAKE out to be (=pretend)</td><td>0</td><td>1</td><td>0</td><td>0</td><td>She's some princess [...] but I think they're <b>making her out</b> to be far worse than what she probably is <b>BNC2014: SAR5</b></td></tr><tr><td>MAKE up for sth. (=compensate)</td><td>0</td><td>1</td><td>1</td><td>1</td><td>You're separated from your family, and so you make friends with other people to <b>make up for that</b> <b>TEC</b>: Join the Team 3e</td></tr><tr><td>MAKE up (of) (=comprise)</td><td>3</td><td>1</td><td>4</td><td>1</td><td>Its collection's <b>made up of</b> over million objects - so we won't be seeing them all today! <b>TEC</b>: Achievers B1+</td></tr><tr><td>MAKE up (=face paint)</td><td>0</td><td>1</td><td>0</td><td>2</td><td>her fear was clowns so they got John to get <b>made up</b> and and dressed as a clown <b>BNC2014: S57J</b></td></tr><tr><td>MAKE sth. up (=invent)</td><td>2</td><td>17</td><td>4</td><td>6</td><td>I always thought they <b>made that up</b> to scare us <b>TEC</b>: Green Line 5</td></tr><tr><td>MAKE sth. up (=prepare)</td><td>1</td><td>1</td><td>0</td><td>2</td><td>I had asked him to <b>make up</b> a fire in my office <b>TEC</b>: New Bridges 2</td></tr><tr><td>MAKE up (=reconcile)</td><td>0</td><td>2</td><td>0</td><td>1</td><td>we had a bit of a bond over that [...] yeah I think you two'll <b>make it up</b> <b>BNC2014: SEPP</b></td></tr><tr><td>MAKE with (=content)</td><td>1</td><td>0</td><td>0</td><td>0</td><td>Oh, I'm sure we can <b>make with</b> what we've got <b>TEC</b>: Green Line 2</td></tr><tr><td><b>Total</b></td><td><b>8</b></td><td><b>26</b></td><td><b>13</b></td><td><b>29</b></td><td></td></tr></tbody></table>

Both the sparsity and the high type-token ratios of MAKE phrasal verbs in Textbook Conversation (five types for eight tokens) and Textbook Fiction (five types for 13 tokens) suggest that school EFL textbook authors do not have a dedicated strategy for exposing learners to the most frequent, useful, or easily acquired phrasal verbs. Indeed, there is seemingly no agreement as to which phrasal verbs are likely to be most relevant or useful to teenage language learners. This finding echoes that of Koprowski (2005: 330) and Gouverneur (2008b: 240), both of whom concluded that
EFL textbook authors did not seem to adopt any coherent criteria for selecting the phraseological items featured in the textbooks they analysed.

Whilst this case study on the verb MAKE cannot pretend to provide a comprehensive overview of the most frequent phrasal verbs involving MAKE in ENL Conversation or Fiction, the following section aims to demonstrate that even the basic criterion of frequency can already provide textbook authors with valuable information as to which relevant phrasemes they could more prominently feature in their publications. For instance, within the 674 randomly collected concordance lines featuring the verb MAKE in the Spoken BNC2014, one phrasal verb clearly emerges as particularly prominent in everyday conversation: MAKE sth. up in the sense of fabricating a story (see Table 36). Indeed, it was found 17 times, whereas all other phrasal verbs within this sample of the Spoken BNC2014 were observed just once or twice (see Table 36). In addition to stories and anecdotes (168)–(169), lyrics, numbers and statistics are also frequently associated with this phrasal verb (170).

(168) he told loads of fibs in well it's not fibs at that age but yeah **he made** a lot of stuff up <BNC2014: S57G>

(169) **you've made that up** that's a load of old twaddle that never happened
<BNC2014: SYX3>

(170) I think there were only about three or four actually businesses mm as far as I could tell and that they were there **making the numbers up** <BNC2014: SP2Y>

Predictably, and indeed as with all the lexical phenomena examined so far, fiction, as a register, displays a broader lexical range of phrasal verbs than conversation. In the Youth Fiction sample, *MAKE sth. up* (= *invent*) is the second most frequent MAKE phrasal verb. With nine occurrences (out of 392 MAKE occurrences), the most frequent is *MAKE out* (= *perceive*). It is used both in the visual (171) and auditory (172) senses. Strikingly, this phrasal verb is not featured in any of the narrative texts printed in the 42 textbooks under study.

(171) Then I see a messenger ride in from the east, but I can't **make out** who it is because I'm looking almost straight into the sun.
<Youth Fiction: Crossley-Holland 2001: The Seeing Stone>

(172) Daphne tried to **make out** what was said next, but people all started talking at once. <Youth Fiction: Pratchet 2001: Nation>
### 5.3.6 Causative MAKE<fnref n="33" />

In 5.3.2 we saw that causative constructions are the most frequent use of MAKE in the Spoken BNC2014 sample and the second most frequent in Textbook Conversation. As compared to the Spoken BNC2014, Textbook Conversation significantly underrepresents MAKE as a causative verb ($p = .011$, OR = 1.36, 95% CI 1.07–1.74). By contrast, no significant difference in causative usage was observed between Textbook Fiction and the corresponding sample of MAKE concordance lines from the Youth Fiction corpus ($p = .64$, OR = .92, 95% CI 0.67–1.26). Since causative constructions represent a highly frequent use of the verb MAKE, and as causative periphrastic constructions are known to cause learners difficulties in terms of both syntactic and phraseological choices (see 5.1.1), this section aims to further explore the syntactic and phraseological representations of MAKE as a causative verb in Textbook Conversation.

Table 37 displays the raw and relative frequencies of the four types of causative MAKE constructions observed in the two conversation samples. These are featured in very similar proportions and none of the differences are statistically significant. However, it is worth noting that all of the verbal MAKE causative constructions in Textbook Conversation are [X MAKE Y $V_{inf}$] constructions (see Table 37). This is interesting because textbook grammars place a strong emphasis on the syntactic difficulties involved in producing causative constructions in the passive voice, e.g., [X BE made $V_{to-inf}$], yet seemingly choose not to model these in textbook dialogues (for more on the representations of causative constructions in EFL textbooks and potential pedagogical implications, see Le Foll forthcoming).

<footnote n="33">³³ Causative constructions across the entire TEC corpus were further analysed in a research article to appear as: Le Foll, Elen. Textbooks and Corpus Linguistics: the case of causative constructions. In Kieran Harrington & Patricia R. Ronan (eds.), *Corpus Linguistics in the English Language Teaching Classroom - Research and Practice*. Palgrave MacMillan.</footnote>