Civilian perceptions of police: A thematic analysis of non-physical encounters with law enforcement
Bibliographic record
Abstract
Literature pertaining to civilian-police relations within the United States primarily focuses on unjust physical treatment of civilians by law enforcement. However, research examining the ways in which adverse nonphysical encounters with law enforcement influence perceptions of police and compromise their relationship with civilians is less prevalent. This study utilizes a thematic approach to analyze 252 participants open-ended responses from the Police–Public Encounters survey. The Police–Public Encounters survey was designed to investigate the prevalence, demographic distribution, and psychological correlates of police victimization from adults across four US cities (Baltimore, New York, Philadelphia and Washington, DC), to understand their most notably adverse encounter with police. Study findings revealed four themes: 1) direct and indirect experiences of racial profiling, 2) fear and intimidation, 3) unjust treatment, and 4) poor quality of service. Findings highlight the relationship between nonphysical encounters with police and notions of procedural justice that influence civilian-police interactions. Implications for future research should continue to explore citizens’ perceptions of police as well as police perceptions of their encounters with civilians to examine how this may affect their ability to serve and protect communities.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".