Bibliographic record
Abstract
Restorative justice has been part of the criminal justice system in the United States for more than three decades. Beginning in the 1970s, this approach emerged as an alternative to the retributive nature of the justice system, which tends to focus on punishment as a response to violations of social norms. In contrast, restorative justice places crime victims at the forefront by encouraging dialogue, reconciliation, and the restoration of relationships between offenders, victims, and communities. This approach aims to create a more holistic resolution and reduce the likelihood of reoffending. This study reviews how Victim Offender Mediation (VOM) serves as one of the main paradigms in the implementation of restorative justice in the United States. VOM allows victims and offenders to engage directly in conflict resolution, resulting in mutually agreeable solutions. In addition, this study compares restorative justice policies in the United States with similar measures in Canada, including the passage of the Youth Criminal Justice Act (2002) which reflects the principles of restorative justice. The results show that restorative justice has the potential to be a more humane and effective approach in the criminal justice system. However, consistent implementation and a supportive legal framework are needed to ensure its sustainability. This study provides important insights for the development of more inclusive and restorative-oriented criminal justice policies in the United States and other countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".