Who is they? Pronoun use across time and social structure
Bibliographic record
Abstract
Who uses they, and who can they be (or not be) used for? Singular they has been proscribed in formal grammars since the mid-18th century, 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; Lagunoff 1997; Matossian 1997; Newman 1992; Strahan 2008). This thesis investigates the envelope of variation (e.g., LaScotte 2016; Maryna 1978; Meyers 1990) in which speakers make choices of third person singular pronouns based on a multiplicity of both linguistic (e.g., gender stereotypicality, antecedent type) and social (e.g., gender, age, LGBTQ2S+ identity) factors. The analysis is based on data from 620 participants from across Canada and the US between the ages 13 and 79. \nAn online survey sought responses related to three occupations: LaScotte’s (2016) open ended ideal student question was replicated, and Martyna’s (1978) fill in-the-blank style was modelled for mechanic and secretary—nouns with observed and unambiguous gender stereotypes (masculine and feminine respectively; Deaux & Lewis 1986; Haines, Deaux, & Lofaro 2016). Participants self-identified their gender and were categorized into a ternary grouping: men (e.g., cis, trans, transmasculine), women (e.g., fem, cis, trans, female ish), and non-binary (e.g., genderqueer, genderfluid). LGBTQ2S+ identity was also collected, as well as personal pronouns. Use of third person pronouns in the survey responses is quantified by consistency (i.e., maintaining use of the same pronoun throughout a participant’s response) and by proportional frequency of use—the latter explored in depth. \nThe most important quantitative finding is that singular they is the most consistently and frequently used third person pronoun overall. But, its patterns of use are not parallel across test occupations or participant social groups. The results indicate that student is gender-neutral, whereas mechanic and secretary remain gendered (he:they; she:they), results that are reflected by perceptual ratings: student remains neutral (they), mechanic skews masculine (he), and secretary skews feminine (she). The impact of social characteristics adds layers of complexity about the groups leading sociolinguistic change at societal levels and/or within their own communities and networks: Non-binary, LGBTQ2S+, users of gender neutral personal pronouns, and/or younger. Collectively, these findings suggest that gender stereotypical roles are not unilaterally weighted and biases can manifest through pronominal choice. There are multiple dimensions of influence, such as the referent, one’s identity, and the communities to which individuals are connected. Thus, this thesis both uncovers persistent gender biases and creates a dynamic display of pronominal variation across speakers.
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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 | 0.009 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".