Exploring the Relationship Between Officer Safety and De-escalation in a Simulated Crisis Encounter
Bibliographic record
Abstract
Recently, there has been an increase in media attention and public interest in the use of de-escalation by police officers; however, concerns have been raised regarding potential risks to officer safety. The literature examining the relationship between officer safety and de-escalation is sparse. Drawing on performance assessments of 122 active-duty police officers during a realistic scenario with a person in crisis, the relationship between de-escalation techniques and officer safety was examined using multiple regression analysis and multiple correspondence analysis; a positive (but imperfect) relationship between de-escalation and officer safety was found. The association between relational de-escalation strategies (e.g., active listening, displaying empathy) and officer safety appeared to be strong; less so for tactical de-escalation strategies (e.g., pre-planning, self-control). However, it is unclear whether relational strategies increase officer safety, or whether greater officer safety allows relational strategies to be used. Future research is needed to understand this relationship and determine whether similar results are found in more naturalistic settings.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".