Constituting Restorative Justice: A Case Study Exploring Volunteers’ Experiences of Meaning
Bibliographic record
Abstract
Restorative Justice (RJ), a model for responding to crime that focuses on addressing harm and restoring the relationships between victim, offender, and community, has gained legitimacy as an effective alternative to lengthy court procedures. As other researchers and RJ theorists have noted, core to restorative justice programing is the inclusion of community members, whether as facilitators in victim-offender conferences, as supporters for offenders in re-entry support circles, or as representatives of community harm in larger sentencing circles. Relying on community volunteers to implement RJ processes has the potential to ensure a core RJ value of increased community involvement in responding to harm and offers a practical mode of supporting this unique response to crime. Despite the value placed on volunteer involvement, the experience of the volunteers who engage with these programs is a significantly understudied aspect of the RJ movement. This research explores the volunteer experience at one of the longest operating RJ programs in North America. Drawing on 16 interviews with volunteers and staff, as well as 35 hours of observation, this research looks at how volunteers frame meaning within RJ and the insights their experience provide about the nature of RJ more broadly. This study traces how volunteer experiences highlight the process by which community members find meaning within RJ through witnessing and sharing narrative of impact, the allure of an RJ when conceived of as an alternative to other models of conflict resolution, and the embedded power relations within the RJ process. As such, it re-centers major debates of the RJ field within the experience of community facilitators and provides significant insights into how RJ is constituted within the volunteer experience.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".