Is evidence-based policing being integrated in to uniformed volunteer policing? An exploratory study from England and Wales
Bibliographic record
Abstract
Abstract Evidence-based policing (EBP) posits that police practices should be based upon evidence from research, and those in policing should be trained to value and utilize sources of knowledge, research, practices, and experiences. Set within the national and international context of EBP and volunteer policing, this paper explores whether volunteer police officers, called special constables (SCs) in England and Wales, are aware of EBP, integrate EBP into their volunteering and the evidence sources utilized. The findings, from a sample of SCs volunteering in England and Wales, demonstrate that over half of respondents have heard of EBP, a significant number suggest regular use, yet some are either not sure or do not use EBP. SCs are expected to take part in similar frontline roles as their regular police colleagues, with the same policing powers and responsibilities. The paper concludes though that EBP is integrated into regular officer training but training of SCs in the adoption of EBP seems more limited. This research is one of the first to examine the adoption and understanding of EBP by volunteers within policing.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".