Bibliographic record
Abstract
“Restorative justice,” as defined in the Social Work Dictionary is “a non-adversarial approach usually monitored by a trained professional who seeks to offer justice to the individual victim, the offender, and the community, all of whom have been harmed by a crime or other form of wrongdoing” (Washington, DC: National Association of Social Workers, 2014. p. 367). This emerging model for resolving conflict and righting a wrong focuses on repairing the harm done by an offense by involving the victim, the offender, and the community. This entry identifies resources on restorative justice theories and strategies with relevance to social workers, mental health professionals, and school and correctional counselors. At the micro level, restorative justice is played out as conferencing between victims and offenders, for example, by way of family group conferences and healing circles. At the macro or societal level, restorative justice takes the form of reparations or truth commissions to compensate for the harm that has been done. The magnitude of the situations covered ranges from interpersonal violence to school bullying to mass kidnappings to full-scale terrorism and warfare. Since in the United States restorative justice has only recently been given formal recognition by the profession of social work, included for the first time the National Association of Social Workers (NASW) Encyclopedia of Social Work in 2008, books and articles that specifically relate restorative justice to social work are scarce, and most are of recent vintage. Accordingly many of the listings in this entry are drawn from criminal justice, legal, and international sources, especially from Canada, New Zealand, and Australia.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.029 |
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".