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Record W4405098771 · doi:10.22215/etd/2022-16191

Exploring the Relationship Between Officer Safety and De-escalation in a Simulated Use of Force Encounter

2022· dissertation· en· W4405098771 on OpenAlexaff
Audrey Diane MacIsaac

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsOfficerModerationPsychologyPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Recently, there has been an increase in media attention and public interest in the use of deescalation by police officers; however, some concerns have been raised regarding the safety of officers when engaged in de-escalation.Drawing on an analysis of officer behaviour during a realistic domestic disturbance scenario, the relationship between de-escalation techniques and officer safety was examined, as was the relationship between de-escalation, officer safety, and various officer factors (e.g., officer sex, training, and education).The results suggested a positive (but imperfect) relationship between de-escalation and officer safety.There were no significant relationships between demographic and training variables on officer use of de-escalation.However, officer sex and level of training were associated with officer safety, with males receiving higher safety scores than females and those with higher levels of training receiving higher safety scores.Finally, only years of police service was a moderator of the relationship between de-escalation and officer safety (for those with fewer years of service, no significant relationship existed, but for those with more years of service a significant positive relationship existed).This study contributes to ongoing discussions around the use of de-escalation and officer safety and suggests that the use of de-escalation does not appear to compromise 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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.455

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 head0.077
GPT teacher head0.283
Teacher spread0.206 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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