Frequency of singular they for gender stereotypes and the influence of the queer community
Bibliographic record
Abstract
Singular they has been denounced in formal grammars since the mid-18th century (see Bodine 1975; Paterson 2014), yet it dates to at least the 14th century (Balhorn 2004; Curzan 2003), persevering in both writing and speech (e.g., Baranowski 2002; Balhorn 2009; Newman 1992; Strahan 2008). Linguistic investigations of pronominal use suggest an envelope of variation (e.g., LaScotte 2016; Maryna 1978; Meyers 1990) in which speakers make choices based on a multiplicity of factors (e.g., gender stereotypicality, antecedent type). The role of LGBTQ+ community remains less examined. Do aspects of identity impact an individual’s choice of pronoun for singular generic nouns (e.g., a student), and is that dependent on the referent’s perceived gender? A 2018 survey garnered responses across Canada and the United States from 623 participants (289 LGBTQ+, 196 “queer-adjacent”, 131 non LGBTQ+, and 7 no response). The stimuli consisted of six filler questions interspersed among three targets (mechanic, secretary, student). LaScotte’s (2016) open-ended student question was replicated, and Martyna’s (1978) fill-in-the-blank style was modelled for mechanic and secretary—nouns with perceived gender stereotypes (masculine and feminine respectively; Haines, Deaux, & Lofaro 2016). Finally, participants rated occupations on a sliding scale for perception of role performance (masculine to feminine, with gender neutral at the midpoint). Quantifying the frequency of third-person pronouns across all occupations resulted in 2249 tokens split between they (n=1712), he (n=269), she (n=189), and he/she (n=78). Overall, singular they prevails. But, its patterns of use are not parallel. Mechanic and secretary remain gendered (he and she respectively), whereas student is gender-neutral (they). These results are reflected by the ratings: mechanic skews masculine, secretary skews feminine, and student remains neutral. Across all occupations, non-LGBTQ+ participants use singular they the least frequently, followed by queer-adjacent participants, and then LGBTQ+ participants. Non-binary participants use singular they at the highest rate amongst the LGBTQ+ participants (i.e., more frequently than LGBTQ+ men and women). Additionally, participation in events has an impact: singular they is used more by those who attend LGBTQ+ events. Thus, this study provides empirical evidence about the multifaceted interactions of identity and network, as well as uncovering persistent gender biases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".