RESTORATIVE JUSTICE CIRCLES AS A METHOD FOR ADDRESSING THE IMPACTS OF CRIME ON VICTIMS, COMMUNITIES, AND OFFENDERS
Bibliographic record
Abstract
Considering the true impacts of crime on victims, offenders, and their communities is a philosophical and abstract process. It is an important process, however, because the way in which a society considers these impacts dictates how that society operates its criminal justice system. Generally speaking, the purpose of a criminal justice system is to reduce crime, and the effects of crime primarily on the victims and their communities. For the last 200 years, the justice system model in the United States has been retributive: it has focused on punishment of offenders in order to reduce crime. Restorative justice circles have been revived or introduced in various communities across North America for several decades, including communities in Manitoba, the Yukon Territories, Minnesota, Alaska, and Massachusetts. This paper describes restorative justice circles and traces its roots in American Indian traditions, explores individual communities using circles and what types of crimes the circles address, and explores the notable successes of circles in each community. To examine how these circles work in the formal legal system, there is a brief examination of Canada and the United States' legal allowances for restorative justice practices. Finally, the potential problems and limitations of the circles are examined so as to give a more well-balanced and fair review of the use of circles.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".