The mandate and activities of a specialized crime reduction policing unit in Canada
Bibliographic record
Abstract
There has been a lexical shift in policing terminology from ‘crime prevention’ to ‘crime reduction.’ Still, the overarching goals continue to include addressing crime and disorder and providing public protection. The Royal Canadian Mounted Police (RCMP) has developed specialized crime reduction units (CRUs) as one strategy to achieve these objectives; however, there has been limited research on these units’ mandates and crime reduction strategies in a Canadian policing context. This paper presents the findings of qualitative interviews and descriptive statistics collected from one RCMP CRU to examine how the Unit’s officers articulated the specific tasks established in their mandate and whether their policing activities reflected the mandate’s distinctive objectives. Results suggest that communication and human resource challenges led to officers’ tentativeness in expressing their Unit’s mandate and the teams’ restricted use of analytical tools, respectively. This research has implications for policing agencies seeking to develop specialized CRUs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".