Restorative Justice and dialogistic exchange
Bibliographic record
Abstract
Italian criminal justice authorities are developing programs for cooperating with mediation services in order to reach victims and offenders more effectively. This includes providing suitable communication skills and techniques vis-à-vis victims, offenders and others involved in the mediation process as well as being aware of how victims and offenders may react to the way restorative justice is applied. The EU 2007 guidelines underscore how lack of awareness in many EU Member States about restorative justice (RJ) needs to be overcome by disseminating information at all levels. In Italy the practice is still used in a fairly restricted way. RJ began as a means of finding an alternative to criminal prosecution and conventional punishment, especially in relation to aboriginal populations, e.g. in New Zealand and Australia, though it is also spreading in Canada, the US and the UK. In RJ stress is put on the victims’ needs, while offenders are the focus in ordinary criminal justice. In this paper discourse and dialogistic exchange are singled out as the chief medium of such negotiations, mainly based on encounters between victims and offenders, ( i.e., “Youth Justice Conferencing”, or “re-integrative shaming” that take place in “peacemaking” or “restorative justice”, or “repair of harm” and “sentencing” circles). Many group conferencing programs rely on scripts and on the presence of circle-keepers or facilitators. Qualitative samples of such exchanges from American contexts are analysed and compared with samples from similar practices in Italy, so as to assess from a cross-cultural stance the negotiation of challenging social relationships, also from the standpoint of interactive frames and knowledge schemas theories. The authors adopt a Critical Discourse Analysis (CDA) approach along with the Appraisal Framework for assessing Attitude.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".