Procedurally just policing and persons in behavioral crises: investigating public perceptions, stigma and emotion
Bibliographic record
Abstract
Purpose The police often respond to persons who are not in direct violation of the law, but are rather undergoing behavioral crises due to mental illness or substance abuse disorders. The purpose of this study is to examine how police behavior influences civilian bystanders' emotional responses and perceptions of procedural justice (PPJ) when officers interact with these populations, which traditionally have been stigmatized in American culture. Design/methodology/approach Using a factorial vignette approach, the authors investigate whether perceived public stigma moderates the relationship between police behaviors (i.e. CIT tactics, use of force) and PPJ. The authors also investigate whether emotional reactions mediate the relationship between police behaviors and PPJ. Findings Regardless of suspect population (mental illness, substance use), use of force decreased participants' PPJ, and use of CIT tactics increased PPJ. These effects were consistently mediated by anger, but not by fear. Interactive effects of police behavior and perceived public stigma on PPJ were mixed. Originality/value Fear and anger may operate differently as antecedents to PPJ. Officers should note using force on persons in behavioral crisis, even if legally justifiable, seems to decrease PPJ. They should weigh this cost pragmatically, alongside other circumstances, when making discretionary decisions about physically engaging with a person in crisis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".