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Record W4381856437 · doi:10.1177/14613557231182298

How emergency response teams access tactical armoured vehicles in Canada

2023· article· en· W4381856437 on OpenAlex
Zachary Towns, Rosemary Ricciardelli, Kevin Cyr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Police Science & Management · 2023
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsRoyal Canadian Mounted PoliceMemorial University of Newfoundland
Fundersnot available
KeywordsProcurementService (business)BusinessPublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.378
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it