Reflections on the Past, Present, and Future of Restorative Justice in Canada
Bibliographic record
Abstract
Restorative justice has been integrated into the Canadian justice system for over 30 years and it is now appropriate to acknowledge the achievements of the past, reflect on its current status, and consider where it may go in the future. Restorative justice evolved from experimentation by justice officials and community members looking for better ways to respond to crime, and there is a great deal of variation in how it is defined, understood, and practised. Provisions of the Criminal Code and the Youth Criminal Justice Act support the use of restorative justice in the criminal context. While restorative justice is being used across Canada and there are signs that it is maturing, there are also a number of challenges it faces, such as the need for ongoing funding and national data collection, and the need to define its relationship with Aboriginal justice and continue to engage victim service agencies. However, with continued leadership and support from community-based agencies, Aboriginal groups, faith organizations, governments, universities, and justice agencies, restorative justice will continue to evolve and expand in Canada.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".