Typologie des noms communs de personne et féminisation linguistique
Bibliographic record
Abstract
Pour l’Académie française, le genre masculin, non-marqué, représenterait à lui seul les deux genres, alors que la marque du féminin serait privative et entraînerait une limitation dont le masculin est exempt, instituant chez les êtres animés une ségrégation. Par conséquent, pour assurer l’égalité, l’Académie recommande que les termes de métier non-consacrés par l’usage soient au masculin. Or, une telle position ne respecte pas les structures linguistiques du français parce que les dénominations professionnelles non seulement sont soumises, structuralement, aux variations morphologiques du genre, mais font partie de classes sémantiques déterminées qui exigent une telle variation et sont assujetties à certaines règles syntaxiques propres. La démonstration en sera faite à l’intérieur d’une étude qui englobe tous les noms communs de personne, offrant ainsi un tableau complet de la représentation humaine.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".