A study of police officers' views and experiences with crime reduction strategies
Bibliographic record
Abstract
The Royal Canadian Mounted Police (RCMP) in British Columbia has recently embarked on a journey to reduce crime, reduce fear of crime, and increase public confidence in the criminal justice system through the implementation of Crime Reduction (CR) strategies that involve targeted police action toward prolific offenders, crime hot spots, and responding to the underlying causes of crime. While the CR effort is new to the province, success is already being declared on RCMP websites. Although property crime rates are down in many CR detachment areas, what police officers in these locations are actually doing to engage in CR is not clear. This major paper examined police officers’ views and experiences with CR Strategies through an empirical survey of the police officers of the Coquitlam RCMP Detachment. The survey results suggested that, although great achievements have been made in incorporating CR in day-to-day policing activities, some gaps remain in the implementation of CR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".