Lessons for Canadian crime prevention from recent international experience. Institute for the Prevention of Crime Review
Bibliographic record
Abstract
Cet article identifie huit principaux éléments de la pratique contemporaine de la prévention du crime qui semblent être associés à la baisse ininterrompue de la criminalité dans la plupart des pays développés de l’Ouest et examine leur pertinence pour le Canada. Parmi ceux-ci, nous trouvons: la collaboration intersectorielle pour mettre en place des interventions multiples et intégrées; un accent sur l’approche de résolution de problèmes; des stratégies fondées sur des données probantes; et des initiatives dirigées par le niveau central mais mises en œuvre au niveau local. La conclusion est que le succès des initiatives canadiennes courantes exigera un leadership national, un cadre d’analyse cohérent et flexible fondé sur la recherche et des pratiques ciblant des résultats qui sont surveillées et communiquées de manière transparente. Tout ceci devra être appuyé par des ressources adéquates et stables, par le développement organisationnel et la formation professionnelle, par la dissémination de pratiques efficaces et une stratégie pour promouvoir l’engagement du public. 1 �The opinions expressed in this paper do not necessarily reflect the views of the AIC or the Australian
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".