Procedural justice versus risk factors for offending: Predicting recidivism in youth.
Bibliographic record
Abstract
Theories of procedural justice suggest that individuals who experience respectful and fair legal decision-making procedures are more likely to believe in the legitimacy of the law and, in turn, are less likely to reoffend. However, few studies have examined these relationships in youth. To begin to fill this gap in the literature, in the current study, the authors studied 92 youth (67 male, 25 female) on probation regarding their perceptions of procedural justice and legitimacy, and then monitored their offending over the subsequent 6 months. Results indicated that perceptions of procedural justice predicted self-reported offending at 3 months but not at 6 months, and that youths' beliefs about the legitimacy of the law did not mediate this relationship. Furthermore, procedural justice continued to account for unique variance in self-reported offending over and above the predictive power of well-established risk factors for offending (i.e., peer delinquency, substance abuse, psychopathy, and age at first contact with the law). Theoretically, the current study provides evidence that models of procedural justice developed for adults are only partially replicated in a sample of youth; practically, this research suggests that by treating adolescents in a fair and just manner, justice professionals may be able to reduce the likelihood that adolescents will reoffend, at least in the short term.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".