SMART on Social Problems: Lessons Learned from a Canadian Risk-Based Collaborative Intervention Model
Bibliographic record
Abstract
Recent years have seen increasing recognition of the utility of multi-agency approaches to community safety and well-being. In Canada, a particular risk-driven collaborative intervention model has gained considerable traction among police organizations and community agencies alike. This model, known as a ‘Hub’, aims to identify and attend to cases of ‘acutely elevated risk (AER)’ of harm before incidents occur. The speed with which the model has been implemented in Canadian jurisdictions speaks to the level of interest, and the level of need more meaningful and efficient collaboration among human service providers (including police) to better use existing resources to serve vulnerable populations. To date, however, the literature on the situation table model in Canada has tended to focus on considerations in the post-implementation phase. We are not aware of any material designed to assist in navigating the multiple and complex considerations involved in the development and implementation of the Hub model. Our aim is therefore to assist police organizations in addressing relevant questions and considerations that arise at the ‘front end’ of the process, and that are generally not addressed in depth in the existing literature. To do so, we present a ‘case study’ of select lessons learned over the course of the recent design and implementation of the Surrey Mobilization and Resiliency Table, the 56th working Canadian Hub model, currently in operation in the city of Surrey, British Columbia.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".