Exploring the Relationship Between Officer Safety and De-escalation in a Simulated Use of Force Encounter
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".