GENDER IDENTIFICATION IN FRENCH: FROM IDEOLOGY TO MORPHOLOGY
Bibliographic record
Abstract
The spread of the feminitives (gender-marked nouns) is a modern trend of the language development resulting from the social processes. It is taking place within systemic identification and validation of the woman in texts. The history of sociolinguistic opposition of the French-speaking society to the use of feminitives and text feminization has significant differences between various French-speaking countries, a subject researched by linguistics, sociolinguistics and geolinguistics. The Canadian province of Québec published its recommendations on use of feminitives as early as in 1979; later they were elaborated, refined and expanded. Swiss Geneva passed provisions for the feminisation of professions in 1988; a respective guide was developed in 1991. Respective Belgian regulations were introduced in 1993. However, all the French-speaking countries recognise France’s right to take any final decision regarding questions of the French language. The country had a waiting attitude and made its first steps towards gender identification in 1984, while the big changes that attracted the attention of the society took place in 1998. Since then detailed revision of the language policy became regular aiming at securing a strong position in the modern world. In 2018, the use of feminitives was ordered to be obligatory in the legal documents. French academic circles stress that “the natural evolution” of the language is taking place.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".