Comment (la) Criminologie s’est approprié la police et la sécurité : émergence et diversification thématique
Bibliographic record
Abstract
La littérature scientifique sur la police et la sécurité a beaucoup évolué au cours des dernières décennies, et les articles et numéros spéciaux de la revue Criminologie offrent un important portrait de ce phénomène. Quatre grandes familles d’études ont traversé les 50 dernières années de cette revue alors que se chevauchent des études sur les fondements d’une criminologie critique de la police, son évaluation quantitative, les transformations du marché de la sécurité et le développement d’un policing plus diversifié et internationalisé. Dans cet article, nous analysons quantitativement et qualitativement le contenu des articles afin de mettre en lumière les principales influences et retombées associées à ces quatre aspects. Cette revue de la littérature permet de circonscrire ces différents champs d’études et de les remettre en contexte avec d’importants événements ayant influencé l’évolution de la police comme acteurs de contrôle social dans nos sociétés.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".