Trajectoires criminelles et récidive des délinquants sexuels adultes : l’hypothèse « statique » revue et corrigée
Bibliographic record
Abstract
De façon générale, l’évaluation du risque repose sur des outils actuariels qui incluent des facteurs statiques ou historiques tels les antécédents criminels. La présente étude réexamine la relation entre les antécédents criminels et la récidive violente/sexuelle en tenant compte de l’aspect dynamique de la carrière criminelle. L’étude pose la question suivante : est-ce que les trajectoires criminelles informent sur les risques de récidive ? L’étude actuelle est basée sur une quasi-population de délinquants sexuels adultes incarcérés dans la province de Québec entre 1994 et 2000. Des analyses de classes latentes ont été réalisées afin d’identifier les trajectoires criminelles violentes/sexuelles. Les analyses de survie indiquent que les trajectoires criminelles informent sur les risques de récidive violente/sexuelle tout en montrant des limites importantes de la prédiction actuarielle.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".