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Evidence-Based Instruction of Police Use of Force

2021· book-chapter· en· W3172766932 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

VenueIGI Global eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSituational ethicsCompetence (human resources)PsychologyMedical educationIdentification (biology)Situation awarenessEngineering ethicsPedagogyEngineeringMedicineSocial psychology

Abstract

fetched live from OpenAlex

A significant body of applied police research has investigated the effectiveness of various use of force (UOF) training approaches that traditionally cover decision making (i.e., shoot/no-shoot), situational awareness, and resilience. However, there remains a lack of established educational standards for police UOF instructors beyond physical and tactical competence, including pedagogical principles to promote effective learning. The authors aim to provide police agencies and UOF instructors around the world with a pragmatic framework of evidence-based training that promotes learning, retention, and practical application. The chapter begins with an overview of essential skills and knowledge related to UOF followed by identification of various methodological approaches suitable for instruction of both novice and expert police officers. The chapter will also outline a train the trainers instructor course that is currently offered at the Police University College of Finland. This chapter informs consistent and adequate pedagogical training for and by UOF instructors.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.147
GPT teacher head0.364
Teacher spread0.217 · 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