Restorative Justice in the Reentry Context: Building New Theory and Expanding the Evidence Base
Why this work is in the frame
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Bibliographic record
Abstract
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." Keywords: restorative justicereentrycivic engagementcommunity building Notes 1. The term "evidence" here should not be narrowly used as a synonym for randomized controlled trials or systematic reviews. A vast array of research methodologies has informed the development of restorative practices over the past two decades. Other, rich research methodologies, including observational (e.g., CitationBraithwaite & Mugford, 1994) and basic science approaches (e.g., CitationWitvliet et al. 2008), have arguably been as important to the development and establishment of restorative practices as "evidence-based" than traditional outcome evaluations. Outcome evaluations are useful in establishing the effectiveness of an approach, but do little to explain how or why an intervention works when it does. These questions are best addressed by other methodologies (see, e.g., Bazemore & Schiff, 2004).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it