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Record W4401943053 · doi:10.22215/apb.v1i.4859

The Need for a Standardized Body-Worn Camera Policy

2024· article· en· W4401943053 on OpenAlex
Alana Saulnier

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied police briefings : · 2024
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer graphics (images)

Abstract

fetched live from OpenAlex

Based on a survey of all federal, provincial, municipal, and First Nations police services in Canada, 36 of 172 Canadian police services are using body-worn cameras (BWCs) as of 2022. Twenty-seven of these services shared their BWC policy with the researchers of the source article. Almost all BWC policies provided activation instructions, required subject notification of BWC use as soon as reasonably possible, did not allow BWC footage to substitute for other forms of evidence, and permitted users to view their BWC footage. However, some important topics were not consistently discussed in existing policies, including issues around camera buffering, victim-sensitive practices, and services publicly disclosing BWC footage in the public interest. Police services should work towards using a nationally standardized BWC policy to promote evidence-based practice, increase public confidence in police, reduce resource wastage in services acquiring BWCs, and decrease liability for services using a shared standard.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.305
Teacher spread0.296 · 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