The masculine bias in fully gendered languages and ways to avoid it: A study on gender neutral forms in Québec and Swiss French
Bibliographic record
Abstract
ABSTRACT The extent to which gender neutral and gendered nouns impact differently upon native French speakers’ gender representations was examined through a yes-no forced choice task. Swiss (Experiment 1) and Québec (Experiment 2) French-speaking participants were presented with word pairs composed of a gendered first name (e.g., Thomas) and a role (e.g., doctor), and tasked to indicate whether they believed that [first name] could be one of the [role]. Roles varied according to gender stereotypicality (feminine, masculine, non-stereotyped), and were either in a plural masculine (interpretable as generic) or gender neutral (epicenes and group nouns) form. The results indicated that the use of gender neutral forms of roles avoided a strong male bias found for the masculine forms, and that both gender neutral and masculine forms used equal cognitive resources. Further, stereotype effects associated with both gender-neutral and grammatically masculine forms were quite small (<1%). These results were highly reliable across both Swiss French and Québec speakers. Our study suggests that gender neutral forms are strong alternatives to the use of the masculine form as default value.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".