Situation tables as the new crime prevention: theoretical underpinnings, strengths, weaknesses, and best practices
Bibliographic record
Abstract
Change in police calls-for-service has resulted in a shift in the role of police through the years. Specifically, the increase in mental health calls for police services has created the Community Safety and Well-Being (CSWB) model. The CSWB model seeks to prevent social disorganization through proactive community efforts. Situation Tables are a risk-driven, collaborative model in Ontario that falls under the CSWB model. Drawing on in-depth interviews from Situation Table coordinators and other participants, this thesis found that risk, collaboration, and harm reduction are the theoretical underpinnings of the Situation Table. Interestingly, participants identified Situation Tables as an alternative to incarceration in that they proactively address criminal risk factors and offer social services rather than engaging in law enforcement action. In addition, this thesis offers recommendations and best practices for the participation and implementation of Situation Tables as a learning tool for current and future Situation Table participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".