Restorative Justice in the Reentry Context: Building New Theory and Expanding the Evidence Base
Bibliographic record
Abstract
Although there is currently considerable activity around improving the reentry process for former prisoners returning to society, much of this work lacks a strong theoretical and empirical foundation. With its well-developed theoretical grounding and its growing evidence base, the restorative justice movement provides an obvious place to start when thinking about reintegration. Yet there has been relatively little application of restorative models in the reentry context. We argue that restorative justice interventions are too often focused on the “soft end” of the justice process, when a growing body of evidence suggests that restorative practices might be more effectively focused on the reintegration process for more serious offenses. We provide examples of Canadian and U.S. programs that could be considered emerging models of “restorative reentry.”
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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 | 0.177 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.013 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".