Restorative Justice, a comparative analysis of discursive practices:\nDialogistic exchanges in the USA and Italy
Bibliographic record
Abstract
Following the 2007 European Union Guidelines for mediation in penal matters from a restorative justice (RJ) perspective, Italian criminal justice authorities are developing programs for cooperation with mediation services so as to reach victims and offenders more effectively. Such programmes need to include common principles concerning the ethics of the mediator, and the appropriate skills and techniques of communication with victims, offenders and others engaged in the mediation process. RJ began as an experiment in alternatives to criminal prosecution and conventional sentencing, especially where aboriginal populations were involved (New Zealand, Australia, Canada, North America). It is fundamentally concerned with re-establishing social equality in relationships by involving the victim, the offender, and the community in a search for solutions which promote repair, reconciliation, and reassurance, with a strong emphasis on the victims’ needs. Hence, discursive practices necessarily play a fundamental role in such negotiations which take place in “peacemaking” or “repair of harm” and “sentencing” circles. Many group conferencing programs rely on scripts and on the presence of circle-keepers or facilitators. The aims of this pilot study were to analyze qualitative samples of such exchanges from non-European contexts and compare them with samples from the initial Italian practices, so as to evaluate in a cross-cultural perspective the negotiation of challenging social relationships. A broad Critical Discourse Analysis approach was utilized, including the Appraisal Framework for the evaluation of Attitude.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".