Misunderstandings, mischaracterizations, and malicious accusations: A reply to Walby’s (2021) <i>SWAT Everywhere?</i>
Bibliographic record
Abstract
To develop a more informed understanding of why tactical officers are used in Canada, we interviewed patrol and tactical officers from three Canadian police services (Jenkins et al., 2020). Interviewees indicated that tactical officers tend to be used on calls that go beyond the capabilities of patrol officers, including high-risk calls and calls unfolding in special environments, and that their use results in reduced threat to police and public safety. In response, Walby (2021) has argued that evidence-based policing (EBP) research of the sort we conducted is inherently biased. He also criticized our understanding of existing literature, took aim at our research methodology and conclusions, and questioned our academic integrity by claiming that we were paid by the participating police services to conduct the research. While Walby makes some valid criticisms of our research, his response is riddled with misunderstandings, mischaracterizations, and malicious (unfounded) accusations. After setting the record straight with respect to allegations regarding our nefarious motives to conduct the research, we argue that Walby completely misrepresents EBP research when he argues that it aims to support harmful police practices in exchange for financial support. We then correct numerous instances where Walby either mischaracterizes existing research or misrepresents our views (and those of our interviewees) when it comes to the use of tactical officers. We conclude by calling for more inclusive conversations to take place to address the issue of police militarization. These conversations must include community members, but they must also include the police.
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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 | 0.067 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.058 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.056 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".