Innovative Community Policing Models in Response to Discrimination of Racialized Youth Who Use Drugs
Bibliographic record
Abstract
Tense relations between police and racialized youth, especially those who use drugs, are ongoing concerns in Canada and other countries, with greater incidents of racial profiling and discrimination resulting in reduced trust and police legitimacy. While there have been calls for various forms of defunding police, some youth who use drugs (YWUDs) have highlighted the need for police to have stronger connections with the community to create better relationships between YWUD and police. The concept of “community policing” may be a viable and promising approach to reimagining law enforcement. A rapid review of grey and peer-reviewed literature was used to highlight promising community policing models, and identify gaps, strengths, and approaches to promote positive relations between police and racialized YWUD. We found that very few programs offered comprehensive, culturally safe training curriculums or initiatives that involve consultation or co-development with community members themselves. Furthermore, few program models are empirically supported by evidence-based outcomes and were largely based on anecdotal evidence. These findings may inform future practice with recommendations for enhanced law enforcement training in trauma-informed harm reduction, youth psychosocial development, prosocial communication and crisis de-escalation techniques, reconciliation, and cultural safety.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".