Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality (2024)

Abstract: People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow “more gendered” than male-dominated ones, and thereby they encourage more gender mentioning overall.

## introduction while people of any gender can do any job, most occupations in the current world are not fully genderbalanced, meaning a statistic association exists between particular genders and particular occupations. we can see evidence of such associations in how humans talk about occupations. when talking about a carpenter who is a woman-a maledominated profession in the us-people may refer to her as "female carpenter". this usage suggests that a woman being a carpenter is rare (and potentially surprising), and thus some find it worth remarking upon explicitly. men on the other hand, are more likely to be called "carpenter" instead of "male carpenter", presumably because the latter feels redundant or overinformative somehow. typicality effects such as these shape the way we use language in general. for example, @xcite adduced evidence for typicality effects figure 1 : we found the strongest correlation between the femaleness of an occupation (according to us labor statistics) and gender mentioning in pushshift.io reddit, a surprising finding to some extent, because it contradicts the idea that gender mentioning occurs when special events are being pointed out. instead, this finding points more to a gender-specific phenomenon. in the color domain, finding that speakers never referred to an image of a lone yellow banana as "the yellow banana", but almost always referred to a lone blue banana as "the blue banana". because we think of the (stereo-)typical banana as yellow, the blueness of a blue banana is more worth remarking upon than the yellowness of a yellow one. in this work, we explore a parallel phenomenon in the occupation gender space: are there gender typicality effects for occupation nouns? if so, we expect more mentions of "male" when an occupation is woman-dominated, and more mentions of "female" when an occupation is man-dominated. alternatively, gender mentioning may occur more often when the gender associated with an occupation is salient. past work in social psychology and gender studies has suggested that gender is generally more salient for women than for men. in particular, men are seen as more typical of the concept of "person" than women are, and that women are more associated with gender than men are @xcite @xcite @xcite @xcite . put simply, according to these theories, the concept of 'woman' differs from the concept of 'man' in that it has an additional semantic attribute that encodes cultural and/or biological facts or connotations (such as information about clothing, behaviors, etc.) that are associated with women. in light of this past work, it is also plausible that associating women with "having gender" affects how we conceptualize woman-dominated occupations, such as "nurse" or "hairdresser". if woman-dominated occupations are indeed conceptualized as "more gendered", then encountering a woman-dominated occupation might make gender itself more salient, and lead to an increase gender mentioning in corpora. were this to be the case, we would see higher overall mentioning of gender (with both "male" and "female" adjectives) for woman-dominated occupations. in this study, we test these two hypotheses about occupation gender typicality using information theoretic approaches to analyze large text corpora. first, we set aside the null hypothesis that gender mentioning (modifying an noun with either "male" or "female" adjective) is completely unrelated to occupation gender (i.e., the empirical gender breakdown in the real population), see section 5.1. next, we test the hypothesis that gender is more salient for women-dominated occupations. this hypothesis is borne out: we find that gender (both male and female) is mentioned more when the occupation is woman-dominated in our largest tested corpus, see section 5.2. we report medium-to-large correlations between femaleness of occupation (the degree to which it is woman-dominated, according to u.s. labor statistics) and overall gender mentioning (@xmath0.49), and for femaleness of mentioning (@xmath1.50), maleness of mentioning (@xmath2.42), respectively. finally, we test whether gender mentioning correlates with surprisingness, or non-typicality, of gender given an occupation. to test this, we define an information theoretic quantity based on the conditional entropy of female/male gendering given a particular job, whereby low occupation genderedness would correspond to a 50-50 gender breakdown. this can be seen as testing the "blue banana" hypothesis for occupation gender typicality, see section 5.3. we do not find strong evidence for this hypothesis, with very weak correlations emerg-ing for two different source datasets, pushshift.io reddit (@xmath3.13) and wikipedia (@xmath4.12). in sum, our results support the idea that occupation gender typicality is related to gender mentioning, but not in the way predicted by the "blue banana" hypothesis. instead, gender mentioning appears to be more related to gender salience for woman-dominated occupations, at least for the the corpora we investigated. to further analyze our results, we also explore three secondary questions. first, we ask: does corpus choice matter (see section 5.4), and find that our effects are replicated in both wikipedia and pushshift.io reddit, but that they are stronger for the pushshift.io reddit dataset. second, we ask: do the empirical estimates of gender breakdown actually reflect the way people perceive the genderedness of occupations (see section 5.5). utilizing a standard word-embedding based method to determine perceived gender, we find that, while similar, perception and empirical observations do meaningfully differ. third, we ask whether there are trends in the meaning of sentences which mention gender and occupations, such as negative sentiment (see section 5.6). ## data sources we use four different data sources for this work, which we discuss in turn below. to address our main research questions, we first find an empirical estimate of occupation gender (from the us bureau of labor statistics). then, we select two source corpora on which to run our analysis: we use wikipedia and pushshift.io reddit, because they are extensive, openly available resources, which are commonly used in the nlp field, and frequently as training data for language models language models (lms). both should ensure a reasonable coverage of occupations with gender mentions for our analysis. finally, we prompt the opensource llama 2 language model @xcite to regenerate text in the style of wikipedia. we call this new dataset llama 2 wikipedia and use it to investigate whether occupation gender typicality measurements change as a result of a corpus being rewritten by an existing language model. 2020 ) i.a., our empirical estimates of occupation gender breakdowns have been obtained from the u.s. bureau of labor statistics foot_2 . these estimates have been collected monthly by the bureau of census on behalf of the bureau of labor statistics based on household survey. this survey, the current population survey provides statistics about worker demographics, among other things, where individuals self-report their (binary) genders and occupations. we utilize labor statistics for 2023 (accessed in late fall). ## methods in this section, we will first describe how observed occurrence counts translate to probability mass functions (pmfs) relating to occupation, gender mentioning and gender. henceforth, we describe how to compute quantitative markers for gender mentioning and gendered occupations, which we finally use to test our hypotheses from in section 1. ## conclusion we perform a corpus statistical analysis of wikipedia and pushshift.io reddit, and find that there is a relationship between gender mentioning and occupation genderedness using information theoretic techniques. unlike in other contexts, we find no evidence that gender mentioning is correlated with gender surprise. instead, we find evidence that gender is more likely to be mentioned for woman-dominated occupations than man-dominated ones.