Proactive Alliance: Combining policing and counselling psychology
Bibliographic record
Abstract
The philosophy of community-oriented policing (COP) has been widely adopted by police departments around the world and has important benefits, such as improving community members’ satisfaction with police and their perceptions of police legitimacy. However, implementing COP is challenging. Police departments report difficulties obtaining the support of officers on the ground and knowing how best to engage communities—which often contain multiple, overlapping, and sometimes competing groups within the same geographic area—in effective problem-solving and crime prevention. This article describes Proactive Alliance, an innovative training program that draws from criminological theory andevidence-based principles in counselling psychology to teach police officers specific, immediately applicable techniques to establish rapport and long-term working relationships with community stakeholders. The training addresses two key challenges of COP: building meaningful collaboration across diverse communities and empowering frontline officers to become change agents in pursuit of the “co-production” of public safety. It builds on the original theory of broken windows policing, which emphasized the importance of harnessing police officers’ personalities to facilitate successful community engagement and crime prevention, and provides practical tools based on those used by mental health professionals to enable officers to engage in active listening, to connect, and to problem-solve with the community while protecting their own well-being. We conclude by describing the potential of Proactive Alliance to strengthen COP and evidence-based policing more broadly.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".