La police devrait-elle cibler les taudis malfamés ?1
Bibliographic record
Abstract
Les modèles de police de résolution de problèmes et de police communautaire sont souvent présentés en opposition l’un de l’autre. Le présent article propose l’évaluation d’une intervention policière qui incorporait des ingrédients des deux modèles et qui avait pour objectif de mettre fin au foyer de désordres causés par un immeuble de location de chambres. L’étude, qui utilise les appels 911 faits par les locataires des immeubles d’appartements d’un quartier de Montréal, départage les effets spécifiques d’une police de proximité et d’une police de résolution de problèmes. Les résultats de l’évaluation indiquent qu’un régime de patrouille intensive stimule la fréquence des appels 911 faits par les citoyens et que la stratégie de profiler un taudis de mauvaise réputation pour faire diminuer les désordres dans l’ensemble du quartier n’a pas été, dans le site observé, particulièrement concluante.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".