The importance of context: re-examining the ‘deployments’ of SWAT teams in Canada
Bibliographic record
Abstract
Based on an analysis of data released through Freedom of Information (FOI) requests, Canadian researchers have suggested that Special Weapons and Tactics (SWAT) teams are no longer exclusively deployed to resolve high-risk incidents but now frequently respond to routine calls that do not necessitate their involvement. Given concerns about these conclusions, we submitted the same FOI requests to the 14 police agencies examined by Roziere and Walby [2020. Special weapons and tactics teams in Canadian policing: legal, institutional, and economic dimensions. Policing and society, 30 (6), 704–719] and worked with the FOI analyst from each agency to ensure that the data were being interpreted correctly. Based on our re-analysis of the FOI-released data, we report on two problems with the conclusions reached by Roziere and Walby: the conflation of incidents where any SWAT officer responds to calls with full SWAT team deployments and the masking of potential risk factors in calls when relying on call type categories. Our findings illustrate the value of police agencies disclosing relevant contextual information to researchers when possible and they reinforce the necessity of collaborating with FOI analysts to better understand the data being released.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".