Examining the Role and Decision-Making of Tactical Officers in Canada: Risk, Response, and Expertise
Bibliographic record
Abstract
Concerns have been raised regarding the use of tactical officers, commonly referred to as Special Weapons and Tactics (SWAT), which center around two issues: (1) the increased use of tactical officers for incidents that are perceived to be low risk and (2) the assertion that tactical officers are primed to use force. However, our understanding of these issues is significantly underdeveloped. The current dissertation seeks to provide an initial understanding of these issues in the Canadian policing context. In Study 1, I present a content analysis of operational data from the Winnipeg Police Service (WPS) to examine 1652 incidents that received a response from tactical officers. Our findings highlight that the initial call type is not a reliable indicator of the risk posed to public or officer safety. In addition, we found that tactical officers primarily responded to high-risk calls in which violence (n = 599) and weapons (n = 820) were reported. Using the same dataset, the results from Study 2 showed that the use of tactical resources, such as the number of tactical officers responding, was associated with the level of risk posed within an incident. Additionally, the tactics adopted by tactical officers varied depending on the presence of risk factors within the call. Study 3 consists of a systematic review of 10 studies that directly compared the decision-making of tactical and patrol officers. I found that tactical officers made more accurate lethal force decisions and were able to do so faster than patrol officers. These differences in performance can be attributed to the expertise of tactical officers, including their ability to focus on relevant information. The final study drew on interviews with 23 WPS officers to develop a theory explaining how officers make decisions during potential use-of-force incidents. Our theory, the process of preserving life, is comprised of two main components: (1) an officer’s decision-making across the encounter in which they assess, anticipate, and act, and (2) incident- and officer-related factors that influence the decision-making process. Officers within our sample exhibited expertise in responding to potential use-of-force incidents and tactical officers possessed unique aspects of expertise.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".