Policing Under Siege: What is Driving the Normalization of Police Militarization in Canada?
Bibliographic record
Abstract
The police and military are considered a feature of modern nation states. Their functions are meant to be separate, with one fighting external threats and the other fighting internal threats. In contemporary times, the line between the military and police has become increasingly blurred, with the police adopting inherently military characteristics. In the Canadian context, the idea that police at the local level are militarized and that there is a particular set of characteristics driving this militarization has remained largely unexplored. By taking an historic and analytical approach, this dissertation explores the militarization of Canadian police and its underlying mechanisms by revealing how the history of Canadian police intersects with their contemporary practices to arrive at militarization. While traditional explanations for militarization suggests that increased crime and violence are forcing police to militarize due to increased dangers, sociological explanations believe that police are militarizing due to an increase in threat perception resulting from the growth of minority groups and economic disparity. Using Peter Kraska’s “Indicators of Militarization” framework, attention will be paid to the acquisition and use of tactical units, armoured vehicles, and assault weapons by Canadian police at the local level, relying on empirical examples of their (mis)use. A pooled-time series panel regression analysis reveals what is driving militarization by revealing if traditional or sociological explanations of militarization are driving the expansion of police militarization into routine policing at the local level. As Canadian police normalize the use of militaristic tools in routine policing, it is imperative that the dangers of this functional change be understood
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".