An application of procedural justice to stakeholder perspectives: examining police legitimacy and public trust in police complaints systems
Bibliographic record
Abstract
Considerable research focuses on the complainant experience with civilian oversight agencies but we know much less about the perceptions of divergent stakeholders on the fairness in quality of decision-making and treatment associated with investigating allegations of police misconduct. Over 150 members of the community, law enforcement, and policy-makers were brought together to collaboratively develop recommendations to improve the transparency, accessibility, and accountability of a Canadian police complaints system (PCS). Using participant observation and survey data, the findings suggest the majority of participants hold negative views due to underlying themes of distrust in the investigation process, a reluctance to report due to inadequate knowledge and a fear of police reprisals, particularly by high risk and marginalised populations. Stakeholder confidence cannot be separated from the principles of procedural justice and due process constraints. Views on the legitimacy of both the police and the PCS are shaped by the absence of procedural justice principles of fairness in treatment and decision-making. Further, citizens appear to confound perceptions of legitimacy of the PCS with that of behaviour during police–citizen encounters. Thus, to increase public confidence the PCSs must work with police services to improve relationships with the community by developing initiatives that target the elements of the procedural justice model separately.
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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 | 0.071 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| 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".