Entre risque objectif et risque perçu : de la nécessaire prise en considération des représentations sociales de la dangerosité pour une optimisation de son évaluation
Bibliographic record
Abstract
Les professionnels de la santé et de la justice sont de plus en plus sollicités pour évaluer le risque de récidive et la dangerosité des personnes qui leur sont confiées. Mais le risque « acceptable » pour les uns n’équivaut pas nécessairement à celui des autres, nos évaluations dépendant de nos perceptions et représentations sociales spécifiques quant à la dangerosité. Ces différentes manières d’appréhender le danger présentant chacune une certaine utilité sociale, leur prise en considération simultanée, rendue possible par un travail pluridisciplinaire, ne peut qu’être source d’enrichissement pour chaque professionnel y participant et d’amélioration de l’évaluation de la dangerosité.
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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 | 0.041 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".