Procedural justice and legitimacy in adolescent offenders: associations with mental health, psychopathic features, and offending
Bibliographic record
Abstract
Procedural justice is defined as the fairness of the process and procedures used to make legal decisions.Theories of procedural justice suggest that when individuals experience respectful and fair decision-making procedures, they are more likely to view the law as legitimate and, in turn, are less likely to reoffend.However, when individuals come into contact with the legal system, they are not blank slates.They possess beliefs, personalities, and characteristics that may systematically influence their assessment of procedural justice and legitimacy.To date, little attention has been paid to the impact of these intra-individual differences on perceptions of procedural justice and legitimacy.Moreover, studies validating models of procedural justice have largely relied on samples of adults.Few studies have examined the relationship between procedural justice, legitimacy, and offending in youth, and none have examined whether procedural justice continues to predict offending when other, well-established risk factors for offending are controlled.The current study followed a sample of 92 male and female youth on probation in British Columbia, Canada, for 6 months.Results indicated that youth who had higher scores on the Massachusetts Youth Screening Instrument-Second Version (MAYSI-2) Drug/Alcohol Use and Traumatic Experiences scales experienced the justice system as less fair and legitimate.Youth who scored higher on the Interpersonal, Lifestyle, and Antisocial subscales of the Hare Psychopathy Checklist: Youth Version (PCL-YV) reported believing less strongly in the legitimacy of the law.Perceptions of procedural justice predicted self-reported offending at 3 months, but not 6 months, and youths' beliefs about the legitimacy of the law did not mediate the relationship between procedural justice and offending.Results also showed that procedural justice accounted 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).Directions for future research and practical implications of these findings are discussed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".