L’influence de la planification linguistique dans la féminisation des titres en France et au Québec : deux résultats différents en ce qui a trait à l’usage
Bibliographic record
Abstract
Notre propos dans cet article est d’analyser le rôle de la planification linguistique pour l’implantation d’usages concrets dans la féminisation des titres. Nous présentons l’état de la question de la planification dans deux points clés de la francophonie : la France et le Québec, ainsi que les normes proposées dans ces deux pays. Ensuite, nous tentons d’établir comment s’emploient certains noms de profession qui se rapportent à la femme, sur la base d’un corpus de presse écrite à caractère informatif. Nous avons constaté que, au Québec, cette planification a supposé une vraie révolution dans l’usage linguistique. En France, cependant, l’évolution du phénomène a eu une trajectoire différente où l’influence de la planification a été minime.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".