L’expertise psychiatrique entre l’évaluation de la responsabilité et de la dangerosité, entre le médical et le judiciaire. Commentaire du texte de Samuel Lézé
Bibliographic record
Abstract
L’étude de l’expertise psychiatrique judiciaire doit-elle être abordée à partir de la problématique de la récidive ? À partir d’un commentaire d’un article de Samuel Lézé, nous soulignons deux confusions à l’œuvre dans le débat politico-médiatique actuel : entre les figures du fou et du criminel monstrueux, d’une part, entre l’évaluation de la responsabilité d’une personne en proie à un possible trouble mental et celle d’un éventuel passage à l’acte qui n’est pas nécessairement criminel, d’autre part. Ces confusions, qui se nouent autour de la notion équivoque de dangerosité, tendent à occulter les enjeux de l’expertise tels que se les formulent les professionnels, avec leurs limites propres.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".