Control of Urban Crimes and Violations through the Lens of Procedural Justice: from Urban Order to Respect for Citizenship
Bibliographic record
Abstract
The occurrence of urban crimes and violations is a reality that has been a constant agenda for public authorities since the emergence of cities. In Iran, the control of these behaviors is designed based on a judicial framework and relies minimally on the voluntary support of citizens. The primary reason for this seems to be the lack of attention to the fairness of the decision-making process and the conduct of authorities, as well as a failure to align with values accepted by citizens. Public authorities' concerns about disruptions to the order they desire have diminished the willingness to implement new approaches, exacerbating the lack of theoretical grounding in control and weakening scientific efforts in this area. This article is developed using a descriptive-analytical method and seeks to answer the question of how much the process of controlling urban crimes and violations in Iran incorporates the elements of procedural justice (respect and fairness) and what the impact of implementing this strategy might be. Initial assessments indicate that the extent to which the control of urban crimes and violations in Iran benefits from the criteria of procedural justice is minimal, and its continuation will deepen the gap between citizens and public authorities, leading to a decrease in compliance with regulations. Implementing procedural justice significantly contributes to streamlining control and enhancing its quality.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".