Public assessments of police during the COVID-19 pandemic: the effects of procedural justice and personal protective equipment
Bibliographic record
Abstract
Purpose The COVID-19 pandemic has presented many challenges for contemporary police. The present research examines public assessments of police responsibility and performance during the pandemic using a procedural justice paradigm. Design/methodology/approach Participants (N = 104) rated images of a police officer, including when using different items of personal protective equipment (PPE), along the core dimensions of procedural justice. Participants then completed survey questions about their assessments of the police’s responsibility and performance during the COVID-19 pandemic. Findings Findings from our regression analyses indicate that participants’ perceptions of procedural justice are positively related to their assessments of police responsibility and performance. Our findings also indicate that participants’ perceptions of procedural justice can be affected by the police’s use of different items of PPE, including face masks, face shields, goggles and medical gloves. Originality/value The present research uses procedural justice, a well-trodden paradigm from past empirical works, to examine perceptions of police amidst a time of much societal change. The findings present important practical implications for police who must continue to manage public perceptions while providing service during the pandemic.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".