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Record W3158618429 · doi:10.1111/lcrp.12191

Advancing police use of force research and practice: urgent issues and prospects

2021· article· en· W3158618429 on OpenAlex

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.

Bibliographic record

VenueLegal and Criminological Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMountain Equipment Co-op (Canada)University of TorontoCarleton University
Fundersnot available
KeywordsUse of forceOfficerPsychologyPublic relationsTask forceEngineering ethicsPolitical scienceApplied psychologyEngineeringLawPublic administration

Abstract

fetched live from OpenAlex

Leading police scholars and practitioners were asked to reflect on the most urgent issues that need to be addressed on the topic of use of force. Four themes emerged from their contributions: use of force and de‐escalation training needs to improve and be evaluated; new ways of conceptualizing use of force encounters and better use of force response models need to be developed; the inequitable application of force, and how to remediate biases, needs to be more fully understood; and misconceptions about police use of force need to be identified and corrected. The highlighted topics serve as an agenda for future research. Such research should provide greater insight into when, where, and why force is used by police officers, and how it can be applied appropriately. If implemented, the practical recommendations included in the contributions should have a positive impact on police performance, public trust and confidence in the police, and citizen and officer safety.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.387
GPT teacher head0.550
Teacher spread0.164 · 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