Feminisation des noms de metier et redaction epicene : Le point sur les propositions faites dans les pays francophones
Bibliographic record
Abstract
La tesi affronta la tematica del sessismo linguistico e analizza le proposte elaborate dalle autorità dei paesi francofoni (Belgio, Francia, Québec, Svizzera) per far fronte a questo problema e garantire una migliore rappresentazione delle donne nella lingua. Dopo una breve spiegazione sul sessismo linguistico, la tesi si sofferma sui decreti, le guide e le circolari ministeriali proposti nel tempo dai singoli paesi, mettendo in luce una sempre maggiore attenzione da parte delle autorità a questa tematica delicata. L'elaborato si conclude con un confronto fra le proposte dei vari paesi, da cui emerge come il Québec sia il paese più all'avanguardia in materia di femminizzazione dei nomi di mestiere, titolo, grado e funzione e la Svizzera lo stato più attivo in quanto a redazione non discriminatoria dei testi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".