Bibliographic record
Abstract
While it might seem that mid-sized agencies typically have greater resources than smaller services, this doesn’t mean they always know where those resources are, or how to best make use of them when it comes to implementing evidence-based policing (EBP). This reality is often reflected in the fact that many larger police services, despite having trained research staff, pracademics, and/or other resources to hand, do not appropriately target problems or test or track new initiatives (Slothower et al, 2015). To illustrate: a recent survey on receptivity EBP approaches by mid-sized Canadian police services found ‘mixed views were expressed by respondents in relation to their agency’s ability to target high priority policing problems and to test strategies for fixing these problems … [and] views were overwhelmingly negative when respondents reflected on how well they thought their agencies track the effectiveness of strategies over time’ (Huey et al, 2017: 544). In this chapter, we focus on identifying existing and potential resources that mid-sized agencies can draw on to implement EBP practices and maximize research creation and use. We also present successful strategies and practices that have been used by police agencies across the globe, and discuss the strengths and limitations of those approaches given resource, workload, funding, and other issues. We explain where challenges might lie for the mid-sized agency, as well as some ideas for surmounting obstacles. And, as in other chapters, we draw on the relevant literature, our own experiences, and the experiences of police officers who are working within the EBP domain, to present some old and some new ideas for implementing different EBP strategies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.118 | 0.021 |
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".