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Record W4402850870 · doi:10.1177/10575677241282011

The Uniforms of Police Emergency Response Team's: A Research Note

2024· article· en· W4402850870 on OpenAlexaffabout
Zachary Towns, Rosemary Ricciardelli, Kevin Cyr

Bibliographic record

VenueInternational Criminal Justice Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsRoyal Canadian Mounted PoliceMemorial University of Newfoundland
Fundersnot available
KeywordsCriminologyEmergency responseRapid response teamPolitical scienceSociologyPsychologyLawMedical emergencyMedicine

Abstract

fetched live from OpenAlex

The primary role of Canadian emergency response teams (ERTs) is responding to dangerous, violent, and high-risk calls for service (CFS) as a patrol support unit. To unpack how ERTs are deployed in Canada, most often to support frontline patrol beyond full team deployments, in the current study we rely on survey data from n = 35 critical incident commanders from across Canada and highlight the variations in ERT uniforms when supporting patrol. Jenkins et al. suggested ERTs are used during CFS that are beyond the capabilities of patrol to resolve optimally or deployed to calls that lack patrol resources. In policing generally, the police uniform is central for recognizing the police, symbolizes membership to an organization, and provides officers with the clothing necessary to perform their duties safely. Existing research suggests the color of police uniforms can affect citizens’ perceptions of the police organization generally, and the individual officer specifically. Yet, little empirical research exists to reveal variations in ERT uniforms and why police services rely on their ERTs to assist patrol beyond full team deployments.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.742

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.222
GPT teacher head0.566
Teacher spread0.344 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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