Bibliographic record
Abstract
Prendre pour objet d'tude la violence sexuelle, c'est d'emble se confronter plusieurs problmes de dfinition. Aucun mot ne suffit le circonscrire, d'o notre choix de titre, trois verbes : abuser, forcer, violer. Chacun de ces termes est tour tour trop restrictif car il n'voque pas l'ensemble des violences considres comme sexuelles, et trop gnral car il s'applique d'autres violences ou transgressions -on peut abuser de la confiance de quelqu'un, forcer un coffre, violer une frontire. C'est sans doute l'une des caractristiques du lexique de la violence sexuelle, et plus gnralement de la sexualit, que d'tre marqu par la banalit, d'avoir des contours flous, et de jouer sur l'euphmisation et le double sens 1 . Beaucoup d'autres verbes apparatront dans les pages qui suivent : rapter, sduire, attenter, corrompre, ou encore exercer une emprise, blesser la pudeur, connatre charnellement et prendre sans consentement -ainsi que leurs quivalents dans d'autres langues, commencer par le latin. tudier la violence sexuelle, c'est ncessairement traquer l'emploi de ces mots dans la documentation historique, reconstituer leur trajectoire sur la longue dure, en noter les changements de sens et de connotation. De
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.462 | 0.295 |
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".