Binary-constrained code-switching among non-binary French-English bilinguals
Bibliographic record
Abstract
This paper presents data on non-binary French-English bilinguals’ metalinguistic analyses of their code-switching behavior in discussing their gender identities. Six non-binary French-English bilinguals were recruited for sociolinguistic interviews via Montréal-based LGBT+ organizations and asked about their experiences using non-binary French and English, especially how they describe themselves in each language. Participants’ preferences for using English to describe issues of gender identity reveals a particular type of topic-based code-switching is utilized in this community—a novel phenomenon that I have deemed Binary-Constrained Code-Switching, where participants switch out of an L1 (French) into an L2 (English) because they perceive their L1 as lacking the appropriate lexicon or grammatical features, specifically non-binary pronouns and gender agreement markers, to index their gender identities. In parallel to their dispreference for using French to describe their gender identities, participants’ preference for using English correlated with their perceptions of English as a more gender-neutral language than French, as well as a language with more linguistic resources—chiefly, vocabulary— to describe LGBT+ identities (c.f. queer). The data presented here not only supplement the primarily binary gender models found in extant studies of socially-motivated code-switching, but also provide greater evidence for the perceptual link between grammatical gender and social gender.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".