A Canadian replication of Telep and Lum’s (2014) examination of police officers’ receptivity to empirical research
Bibliographic record
Abstract
Research conducted in the United States (U.S.) suggests that many police professionals are unaware of, or resistant to, empirical research, and see little value in adopting evidence-based approaches for tackling policing issues. To determine whether similar views are held by Canadian police professionals, 598 police professionals (civilians and officers) from seven police services across Canada were surveyed. The survey was designed by Lum and Telep (n.d. Officer receptivity survey on evidence-based policing. Fairfax, VA: Center for Evidence-Based Crime Policy, George Mason University) to determine respondents’ knowledge of, and support for, evidence-based policing (EBP). Using their survey allowed us to compare our results to the data they collected in the U.S. Although Canadian respondents had similar concerns regarding EBP as those in the U.S., in several ways, Canadian police professionals were more open to the idea of EBP. The results are encouraging, but still suggest a lack of buy-in from some police professionals in certain regards. Potential reasons for the cross-national discrepancies, and the consequences of the findings, are discussed.
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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 | 0.061 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".