Grammatical Gender Affects Bilinguals’ Conceptual Gender: Implications for Linguistic Relativity and Decision Making~!2008-03-31~!2008-10-13~!2008-11-26~!
Bibliographic record
Abstract
We used a non-linguistic gender attribution task to determine how French and Spanish grammatical gender affects bilinguals' conceptual gender. French-English and Spanish-English bilingual, as well as English monolingual adults were asked to assign a male or female voice to 32 color drawings depicting people, animals, and common objects. French-English and Spanish-English bilinguals classified items according to French and Spanish grammatical gender respectively. This effect was replicated for French-English bilinguals on those items whose grammatical gender was opposite in French and Spanish. Unexpectedly, Spanish gender similarly affected classifications by Spanish-English and English-Spanish bilinguals, as well as English monolinguals. We discuss how grammatical gender, possible covariates, and the order of L1 and L2 acquisition, affect conceptual gender as well as implications for decision making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".