Contain and Callout: A Critical Investigation of SWAT Team Use of Force Reporting and the Limits of Institutional Statistics
Bibliographic record
Abstract
ABSTRACT Police tactical units, commonly referred to as special weapons and tactics (SWAT) teams, are the primary focus of the literature investigating police militarization in Canada. While the contemporary debate surrounds their proliferation and expansion, investigations into tactical unit use of force are notably absent from the existing literature. This work analyzes over 1100 tactical unit use of force incidents from seven large municipal police services in Ontario, Canada, between January 1, 2020 and December 31, 2022. The findings illustrate widespread underreporting of use of force, discrepancies in force reporting, and how the proliferation of police militarization has outpaced mandated reporting frameworks. These issues in reporting suppress use of force statistics and demonstrate that tactical unit use of force and, by extension, the application of militarized technologies, cannot be accurately enumerated. These results are contextualized using critical approaches investigating the social and political construction of institutional statistics, suggesting these statistics maintain the state's existing governance strategies. This paper argues that the underreporting of use of force legitimizes police militarization, providing the state with evidence to justify its expansion and counter claims of its proliferation and potential for social harm.
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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 | 0.043 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| 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".