The Challenges of Institutionalizing Comprehensive Restorative Justice: Theory and Practice in Nova Scotia
Bibliographic record
Abstract
The Nova Scotia Restorative Justice Program ("NSRJ") is one of the oldest and by all accounts the most comprehensive in Canada. The program centres on youth justice, and operates through referrals by police, prosecutors, judges and correctional officials to community organizations which facilitate restorative conferences and other restoratively oriented processes. More than five years of NSRJ experience with thousands of cases has led to a considerable rethinking of restorative justice theory and practice in relation to governing policies, standards for program implementation and responses to controversial issues. The purpose of this paper is to explore the significance of the Nova Scotia experience to date for sustaining restorative justice beyond the pilot project stage, where a vision of community-based justice is institutionalized with the support of considerable state resources. The first part of the paper explains the genesis, structure, theoretical goals and empirical evaluation of the program to date. The second part examines some of the challenges of institutionalizing comprehensive restorative justice. The paper concludes with general observations about the broader implications for restorative justice theory and practice of the Nova Scotia experience.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".