How emergency response teams access tactical armoured vehicles in Canada
Bibliographic record
Abstract
As Canadian police services rely on their emergency response teams (ERT) to respond to different calls for service, their reliance also requires police services to possess the equipment necessary to support their ERT. Since 2004, an ongoing trend remains that police services procure tactical armoured vehicles (TAVs) for their ERTs. In the current article, we explore trends in the procurement of TAVs by Canadian police services comparatively, drawing on two distinct data sets. The first is a content analysis derived from news media and the second is the result of a survey of ERTs across Canadian police services. Our purpose is to explore different trends in the procurement of TAVs by police services, looking comparatively at secondary sources and primary data to better understand the composition of ERTs, the positioning of TAVs within tactical policing and shed light on whether some TAVs are procured more often than others. Discussion centres on the relationship between TAVs and ERT –the need versus desire for TAVs – as well as how policing needs are interpreted and impacted by calls to defund 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".