La féminisation de quelques noms de métiers dans Le Monde en France et Le Devoir au Québec au début du 21ème siècle.
Bibliographic record
Abstract
Dans ce mémoire, nous avons étudié la féminisation de neuf noms de métiers dans un corpus délimité du Monde en France et du Devoir au Québec pour les trois périodes de 2001, 2010, et 2019, et les facteurs qui la conditionnent. Nous avons examiné les approches différentes dans les deux entités francophones, et ce que dit la grammaire française sur le genre grammatical et la forme féminine. Notre but a été de déterminer les obstacles au développement de la forme féminine, et, en suivant la méthode de la linguiste Itsuko Fujimura, d’essayer de comprendre les facteurs linguistiques qui jouent un rôle dans le progrès parfois lent de cette féminisation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".