Quantitative metrics to the CARS model in academic discourse in biology introductions (2024)

Abstract: Writing research articles is crucial in any academic’s development and is thus an important component of the academic discourse. The Introduction section is often seen as a difficult task within the research article genre. This study presents two metrics of rhetorical moves in academic writing: step-n-grams and lengths of steps. While scholars agree that expert writers follow the general pattern described in the CARS model (Swales, 1990), this study complements previous studies with empirical quantitative data that highlight how writers progress from one rhetorical function to another in practice, based on 50 recent papers by expert writers. The discussion shows the significance of the results in relation to writing instructors and data-driven learning.

## introduction the research article is one of the most, if not the single most, important genres in academic discourse. the introduction section in the research article is often reported to be difficult to write @xcite . scholars have long recognized the central role of rhetorical moves in academic writing. the widely known analysis of the structure, the "create a research space" (cars) model @xcite is the de facto standard in genre studies in academic discourse, alongside with the metadiscourse model by @xcite . @xcite 's cars model observes the common pattern found in academic research articles, which encompasses three rhetorical moves (that can be seen as any textual unit, often one or more sentences, that aims to fulfill a particular function for a text). each move can be decomposed to finer steps, while some steps are "optional", and some "obligatory" or expected. in the teaching setting, these moves and steps can be used to guide novice authors in presenting the context, purpose, objectives, literature review, and overall significance of their research logically and persuasively. the moves and associated steps (in bracket) are establishing a territory (define the field, provide background information, set the context), establishing a niche (identify a gap, problem, or unanswered question), occupying the niche (clearly state the purpose, focus, and objectives), reviewing previous research (summarize relevant literature, critically review existing research), and establishing the significance of the research (demonstrate the importance within the broader context). a prevalent strand of studies under this tradition focuses on the correlation between particular rhetorical moves (e.g. establishing a niche or occupying the niche) and linguistic forms (e.g. frequent words or formulaic language, such as the n-gram "the aim of the study"). beyond the study of lexical bundles, scholars often investigate the organizational structure of various parts of research articles from a qualitative perspective, while using empirical corpus data. our study focuses on the structure of the introduction section from an annotated corpus of biology research articles written by expert writers. while previous studies have investigated the same phenomenon, few works investigate the cooccurrence or relation between moves and steps at scale. for example, @xcite adopts a qualitative and manual close reading method with a few texts) for biology texts. in some cases, the focus is on the implementation of moves in actual linguistic forms @xcite , and the dataset were not made publicly available to facilitate follow-up studies or replication. as such, there is no existing dataset with clear annotation of the rhetorical moves. this study presents our analysis of a small dataset of 50 texts in biology as a proof-of-concept and proposes two quantitative metrics to conduct move-step analysis. the contribution of this paper is two-fold: first, we discuss quantitative mea-sures that allow for genre and rhetorical analysis without close reading by researchers, which is timeconsuming and requires expert knowledge of genre analysis. second, we outline our efforts in making the materials useful for writing instructors and novice learners of academic writing in higher education environments. ## related work using corpus data to facilitate understanding of academic discourse is no novel approach. specific to the english for academic purposes (eap) community foot_0 , there has been many corpora like the british academic written english (bawe) corpus @xcite , michigan corpus of spoken academic english (micase) @xcite , and the michigan corpus of upper-level student papers (micusp) @xcite . these resources have been widely used in the eap community for analyzing academic language to facilitate materials development and instructions. the wider coverage of various disciplines means that the data are discipline-agnostic and capable of showing the overall patterns in the language of academic discourse. to better understand rhetorical strategies through the cars model, scholars have also employed corpus tools to investigate the use of common phrases associated with specific rhetorical moves. for example, combinations like "in this paper we present" and "it is well known that" are often found in the introduction @xcite . similarly, jalali and moini (2014) identify 161 common lexical bundles (i.e. frequent combinations of lexical items) in the introduction. the most frequent ones in their study are often related to stating the purpose of the study, such as "the aim of the", "the objective of this", "study was to evaluate". pérez-llantada (2014) compares the skills in native and non-native speakers' of using formulaic combinations, using similar methods. while these findings provide solid evidence from attested examples used by writers, they are also limited in not addressing the organization of the introduction, which is reported to be a common issue @xcite . focusing on the organization and sequencing of the steps, scholars have also investigated how closely writers actually follow the cars model in their practice. previous studies have suggested that expert writers do not follow strictly the cars model in their introductions @xcite . meanwhile, articles from different disciplines may display variations, e.g. applied linguistics @xcite , computer science @xcite , engineering (kanoksilapatham, 2015), and mathematics @xcite . @xcite discusses how introductions and abstracts of wildlife behavior and conservation biology, two closely related branches of biology, also show deviations from swales' cars model. similarly, milagros del saz @xcite suggests that there are particular step-combinational patterns used (i.e. how rhetorical steps are assembled together) for achieving a variety of communicative purposes in agriculture. ## method a total of 50 manuscripts from biorxiv foot_2 were downloaded. from each of the five categories (animal behavior & cognition, biochemistry, biophysics, ecology, and physiology), ten papers were randomly selected and annotated by the researcher. the annotation is based on the original model by @xcite foot_3 , which includes three 'moves' essential to the introductory text, which can be further broken down into steps or options. in this study, each sentence is annotated with step label. the details are listed in ## results taken the introductions of all the 50 articles together, the annotated small corpus contains 43,187 words and 1,297 sentences in total. each category is represented by introductions of 10 articles. ## discussion in the eap community, studies on rhetorical moves are abundant, especially with the focus on the correlation between lexical bundles and particular rhetorical moves, i.e. what phrases appear in which moves/steps @xcite @xcite @xcite . to complement this strand of research that focuses on language use, the present study discusses the progression of the moves and steps. by introducing quantitative measures, we have identified the distribution of specific steps, as well as how different steps may collocate with each other. potentially, a scaled up version using similar methods will be able to identify any microvariations across sub-disciplines, as some previous studies suggest. our results also confirms what @xcite argues with regard to the deviations from the classic cars model. in our sentence-by-sentence annotation, it is often found that move-1 step-3 ("reviewing previous research") is interspersed with other moves. it can be explained by the need to provide further support from previous studies, once the authors have made topic generalizations (see bigram "1b-1c": @xmath0: @xmath1. while the quantitative results from the step-ngram and lengths of steps may seem mundane, novice scientific writers can use these numerical results as quick reference. the attested data in the annotated corpus will also facilitate material development. rather than prescribing to students 7 that the introduction must follow a certain pattern, students can see both conformity to and deviation from the standard cars model. this allows students to gain better understanding of how expert writers may consciously depart from the cars model. given the internationalization of many institutions and the increasing needs for support in academic literacy to both students and early career researchers, the findings here may also mean that instructions to discipline-specific writing should be more fine-grained. for instance, students in biodiversity would have different needs and writing models from students in molecular biology. annotated corpus data will allow instructors to easily find attested data for various needs of students. ## conclusion and future work this study has shown results from a small annotated corpus and how they enhance our understanding of academic discourse through the lens of the cars model. the study bears implications on our understanding of progression in rhetorical across steps (through step collocation) and implementation of steps (through lengths of steps), which in turn benefits teaching of academic writing. in future research, it may also be interesting to investigate whether there is any significant differences between preprints (e.g. from biorxiv as in the present study) and published research articles. while both kinds of data are supposed to be written by advanced or expert writers, there appears to be little research on the contribution of peer review and editing specific to the rhetorical quality of the articles. we acknowledge that the dataset is limited by its size and the single annotator, and intend to remedy these limitations in our ongoing work. in future work, we aim to enhance the efficiency of the annotation process through the application of semi-supervised learning techniques. this involves leveraging the manually annotated corpus to develop an enriched corpus. for example, training a knn model will be useful for the multi-class task that classify the sentences into the various steps. additionally, we can also implement fewshot learning methodologies with the moves and steps being vectorised with pre-trained llms, such as gpt @xcite , on the modest "labelled" dataset to develop machine learning models graduate level of msc in biology programs, with a mix of l1 and l2 users of english.