The impact of victim‐offender mediation: A cross‐national perspective
Bibliographic record
Abstract
Abstract The field of victim‐offender mediation, now in its third decade, has grown extensively throughout North America and Europe, with programs in more than twelve hundred communities. This article reports on results from three related studies on the consequences of participating in victim‐offender mediation, including programs in four of the United States (Umbreit, 1996, 1994a, 1994b; Umbreit and Coates, 1993), four provinces of Canada (Umbreit, 1999, 1995c), and two cities in England (Umbreit and Roberts, 1996). VOM is implemented differently in various places, reflecting cultural norms and mores. Given innumerable ways of doing victim‐offender mediation, are there common experiences shared among participants that can inform program delivery and justice policy? The VOM model was found to be a highly transportable and flexible program in diverse settings. High levels of client satisfaction with both the process and outcome were found, and a high level of fairness was expressed. VOM continues to be a promising model, reflecting the principles of the restorative justice movement and offering a firm foundation of practice wisdom and research from which other newer forms of victim‐offender dialogue, such as family group conferences, circles, and boards, can benefit (Bazemore and Umbreit, 1999).
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".