La dynamique de la criminalité à Montréal : l'écologie criminelle revisitée
Bibliographic record
Abstract
Cette étude de géographie criminelle urbaine explore la possibilité de combiner le concept de désorganisation sociale et la notion d'opportunités criminelles dans un même cadre théorique. À l'aide de modèles linéaires hiérarchiques (MLH), il est démontré que le nombre de crimes commis, tant contre la personne que contre les biens, est mieux prédit par la présence simultanée de délinquants motivés et d'opportunités criminelles. Mais la théorie des opportunités criminelles n'explique pas la présence de délinquants motivés. La théorie de la désorganisation sociale explique beaucoup mieux la distribution géographique de délinquants motivés dans l'espace urbain. Suivant la formulation initiale de Shaw & McKay, les résultats de l'étude indiquent qu'il n'existe pas un lien direct entre la composition des populations résidentes et la criminalité commise dans un quartier. Son effet est indirect et passe par la concentration urbaine de délinquants potentiels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".