Penal Legislation Inflation and Convergence in the West: A French Example
Bibliographic record
Abstract
Cet article analyse les textes légaux de trois États occidentaux afin de documenter l’inflation pénale (l’augmentation du nombre de lois pénales) et la convergence (l’adoption de lois pénales similaires) en France depuis 1992. En utilisant une méthode systématique et quantitative, nous montrons que cette inflation concerne un petit nombre de sujets, d’importance nationale ou supra-nationale. Nous trouvons un taux constant d’inflation pénale aux États-Unis, tandis qu’en France, cette inflation s’est considérablement accélérée dans les années 2000. Puisque les taux de violence, qui sont les mieux illustrés par les taux d’homicide, sont synchronisés, ce décalage des taux d’inflation pénale suggère que celle-ci est plutôt liée à la perception de la sécurité en tant que problème majeur sur l’agenda politique qu’à une réponse à une violence réelle.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".