Hungarian vs. American mediators and how to make communities more resilient
Bibliographic record
Abstract
Restorative justice practices are used in a wide array of criminal offence cases globally as it puts the need of victims and the community at the centre of the proceedings and focuses on repair and rehabilitation rather than judgement and punishment. This study focuses on the different experiences of mediators in Hungary and in Bloomington, Indiana, United States. Two local government offices in Hungary and a non-profit organization, called Community Justice and Mediation Center (CJAM) were selected for this study. Six Hungarian and five American mediators from the local government offices and CJAM were interviewed in person and online. Analyzing the interviews, we find that there are fundamental differences between the definitions, legislation, and the practices used in the two jurisdictions. The training of mediators is found to be similar in both countries but the way restorative practices are used is different. The system in Bloomington allows the process to be more flexible whilst in Hungary, the high caseloads and strict timeframes of the prosecutor’s office demand that cases be very quick and efficient. This is likely the reason why at CJAM, co-mediation is the norm, with at least two but sometimes three or four facilitators working on a case, while in Hungary co-mediation only happens in the most complex cases. However, it is apparent that the goal of mediation and restorative justice meetings is the same in both Hungary and Bloomington: to repair the harms and to help build a better community.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".