Law enforcement agencies’ approach to de-escalation: Incorporating a social services perspective
Bibliographic record
Abstract
In this critical review and social innovation narrative, we analyze the literature regarding Canadian law enforcement agencies’ approach to de-escalation and crisis intervention. Using an interdisciplinary approach, we consider how the skills and values of social work can be used to inform and train officers on essential skills such as de-escalation and conflict resolution. We look at the systemic barriers to bringing about change within Canadian police forces as the current culture continues to be influenced by colonization and law enforcement continues to value and endorse use of force over de-escalation. While services can benefit by applying an interdisciplinary lens when training officers, the factors that impede this union and collaboration are discussed and explored as police services are given immense discretion in how they train and respond to mental health crises. In conclusion, we examine the government’s role in perpetuating these issues.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".