Bibliographic record
Abstract
Dissatisfaction with traditional forms of crime control in the context of youth offending has led to the exploration of alternative paradigms such as restorative justice. This study outlines restorative justice as both a theory and set of penal policies before discussing its practices. Also, this study examines the relationship between restorative justice and desistance. Desistance is both an outcome measure of restorative justice as well as a paradigm of its own. This study finds that restorative justice practices produce desistance across a range of practices and contexts regarding youth offenders. Differential effectiveness, long touted as a pitfall of restorative justice research is also addressed. This study finds that despite variations in restorative justice practices as well as differences in the offenders that participate in them, overall, restorative justice has statistically significant effects of the production of desistance. This research also outlines a more qualitative understanding of how restorative justice mechanisms work with young offenders in producing desistance. Concepts such as reintegrative shaming, the role of dialogue, systems, of support, and procedural justice are described. This research concludes by offering suggestions for research in the future, such as the effect of socio-economic status on restorative justice outcomes, a greater degree of variation in sample size, and a focus on conducting and citing randomized trials. Further use of restorative justice as well as further research into its implementation and data collection is encouraged, as well as a need to examine the applicability of restorative justice in minority contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".