‘If You’re Gonna Make a Decision, You Should Understand the Rationale’: Are Police Leadership Programs Preparing Canadian Police Leaders for Evidence-Based Policing?
Bibliographic record
Abstract
Abstract Recently, we have seen a steady growth in the number of police practitioners and agencies adopting evidence-based policing (EBP). At its core, EBP rests on a central tenet: police decision-making should be ‘based on scientific evidence about what works best’ (Sherman, L. W. (1998). Evidence Based Policing. Washington, DC: Police Foundation). While this proposition seems straightforward, it places a responsibility on police leaders for which they may be unprepared. Understanding how best to commission, resource, appreciate the strengths and limitations of and/or make actionable the products of research, requires senior officers to have some level of familiarity with the research process. One potential source of that knowledge is police leader training and education. However, no one has yet explored the question of whether police leadership programs are adequately preparing senior officers for the world of EBP. To examine this issue, the authors present the results of an analysis of 29 in-depth qualitative interviews with senior Canadian police officers.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".