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Record W4401407116 · doi:10.1093/police/paae084

Exploring the use of conducted energy weapons on subjects with a perceived mental condition

2024· article· en· W4401407116 on OpenAlexaff
Victoria A. Sytsma, Erick Laming

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsTrent UniversityQueen's University
Fundersnot available
KeywordsOfficerPsychologyMental illnessPossession (linguistics)Law enforcementEnforcementSocial psychologyPsychiatryMental healthPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Given the prominence of the CEW in law enforcement, and the gaps in evidence around CEW use on persons with mental illness, using police administrative data we investigate whether perceived civilian mental condition influences CEW effectiveness, and how (as indicated by severity of application mode) and why CEW was used. We find subject mental condition is not a significant predictor of CEW effectiveness but is the strongest predictor of CEW application severity, and of CEW display or deployment for the purpose of both protecting the officer and protecting the public—even when controlling for subject resistance level and weapon possession. Results are discussed.

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 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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.311
GPT teacher head0.441
Teacher spread0.130 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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