Gender diversity and morphosyntax: An account of singular <i>they</i>
Bibliographic record
Abstract
As one of the primary means of constructing gendered identities, language is a matter of central concern to transgender people (Zimman 2018). In this paper, we present an analysis of non-binary singular they; that is, they as used to refer to individuals whose gender identity is not, or is not exclusively, masculine or feminine. Despite singular they’s widespread usage and long history in English, not all speakers judge this most recent innovation to be grammatical, even if they do not object to singular they in quantified, generic, or otherwise gender non-specific contexts, and even if they produce the latter sort of examples natively. We argue that resistance to this new use of they can, at least in part, be attributed to speakers’ level of participation in a grammatical change in progress. Further, we propose that this change can be categorized into three distinct stages, with they’s most recent broadening – that is, as a non-binary singular pronoun of reference – dovetailing with wider socio-cultural changes (as well as featural changes beyond the pronominal system) that underscore the difficulty in separating grammatical and social judgements. As we aim to show, linguists from all subdisciplines – both theoretical and applied – are especially well suited to leverage theoretical insights to advocate for trans-affirming language practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".