Contributions of Evaluation Research to the Development of Community Policing in a Canadian City
Bibliographic record
Abstract
Abstract: This article reviews evaluation research on community policing (CP) in the City of Windsor, including the Windsor Police Service’s utilization of evaluation evidence. It examines five studies of the initial year of two CP patrol units, one beginning in 1990 and the other in 1992. The studies included surveys of the CP-related attitudes and experiences of constables, community residents, and managers of small business, as well as time series analyses of crime trends. The results provided favourable process and outcome evidence. By 1995, more than half the city population was serviced by CP units, and follow-up time series analyses reconfirmed the earlier encouraging crime trends findings. However, in contrast to the positive beginnings, a 1997 survey of the service’s membership revealed no strong support for CP, including an apparent division among management in attitudes about CP. Recent CP developments in the service are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.133 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".