Bibliographic record
Abstract
This paper is a response to an article on public police special weapons and tactics (SWAT) teams written by Jenkins and colleagues (2020). Jenkins and colleagues are responding to a study showing that tactical units and members are being used more in Canadian policing. For Jenkins and colleagues, not only are SWAT teams being used properly, but drawing from interviews with tactical members they suggest SWAT teams should be used more in the future. This response focuses on conceptual, methodological, and empirical deficiencies in the work of Jenkins and colleagues. This response shows that Jenkins and colleagues ignore social theory, ignore relevant contrary data, are ignorant of the harms of policing, and are ignorant of the violence that Black and Indigenous peoples face from Canadian police. Relatedly, this response offers a criticism of what is called evidence-based policing scholarship. Using the work of Jenkins and colleagues as an example, the argument here is that evidence-based policing scholars are in a conflict of interest because of how closely they work with police and due to the funding they receive from police agencies and justice ministries. This conflict of interest decreases the credibility and trustworthiness of the claims of evidence-based policing scholars. Overall, this response draws attention not only to the harms of public policing and criminalization, but also to how evidence-based policing scholarship is supporting the expansion of violent, harmful, and regressive forms of social control.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.039 | 0.081 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".