A quantitative study of Prince Albert’s crime/risk reduction approach to community safety
Bibliographic record
Abstract
Faced with escalating crime rates and increasing demands for services, the Prince Albert Police Service led a mobilization effort to implement a crime/risk reduction strategy called Community Mobilization Prince Albert (CMPA). This study examines the evolution of crime prevention practices from traditional police-based practices that rely on focused enforcement practices, to the emerging risk reduction model, wherein police-led partnerships with community agencies are developing responses to the unmet needs of individuals and families facing acutely elevated risk (AER). These community mobilization strategies have resonated with justice system stakeholders throughout Canada, diffusing throughout the nation in a relatively short period of time. This study examines the outcomes of these crime prevention efforts and their results on reducing crime and social disorder and the associated costs of crime to society, after implementation of CMPA in 2011. In order to evaluate the crime reduction efficacy of this approach, crime rates and the costs of crime were examined prior to the adoption of the mobilization efforts and afterwards. We find a statistically significant decrease in the rates of violent and property crimes after the introduction of the community mobilization approach, and the costs to society of these offences also decreased. Given those findings, a number of implications for policy, practice, and future research are identified.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".