Oh, Canada ! La baisse de la criminalité au Canada et aux Etats-Unis entre 1991 et 2002
Bibliographic record
Abstract
Après des décennies d’augmentation constante de leur criminalité, le Canada et les États-Unis connaissent des baisses importantes depuis le début des années 1990. Plusieurs explications ont récemment fait surface aux États-Unis sur les facteurs pouvant rendre compte de cette baisse, dont une augmentation du nombre de policiers, l’application par les forces de l’ordre de méthodes plus répressives et l’augmentation importante du taux d’incarcération. Notre analyse des tendances de la criminalité entre 1991 et 2001 pour quatre types d’infractions indique que des baisses comparables de la criminalité sont observables au Canada sans que des changements notables en termes de répression policière ou d’incarcération soient notables. Ainsi, les explications démographiques, économiques et culturelles semblent plus aptes à rendre compte des baisses de la criminalité en Amérique du Nord.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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".