Bibliographic record
Abstract
Depuis plus de trente ans, le francais connait actuellement un grand bouleversement linguistique en tant que la feminisation des noms de metiers et de titres. En France, des mots masculins sont utilises pour designer les femmes accedant a des positions sociales jusque-la occupees pas des hommes. Les partisans de la feminisation revendiquent l``utilisation des denominations feminines pour designer des femmes. Ils avancent les principaux augumentaires : la visibilite que les formes feminisees apportent aux femmes, la promotion de l``egalite des sexes sur la scene et principalement dans le monde du travail et le respect de l``identite des femmes. Les opposants, de leur cote, avancent differents argumentaires, qui relevent de la linguistique et de l``ideologie. La France refuse la feminisation pour le concervatisme du francais, le Quebec applique systematiquement la feminisation autant aux textes qu``aux noms de metiers. Les langues connaissent en permanence un bouillonnement de variantes geographie, sociales, individuelles parce qu``elles sont traversees d``influences diverses. La feminisation des noms de metiers est en evolution. Nous en esperons le resultat judicieux pour les apprenants de FLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".