Bibliographic record
Abstract
Restorative justice emerged in the 1970s as a way to bring peace to troubled relationships and communities by setting conditions that would promote mediated dialogue between offenders and victims. 1 As the movement gathered momentum in the 1980s and 1990s, its usefulness was extended beyond criminal cases to more complex social settings.Various models of restorative justice creatively built on earlier nonwestern practices of peacemaking and post-confl ict reconciliation, e.g., Canadian practices have adopted aboriginal "peace circles," and New Zealanders have incorporated Maori practices of communal dialogue.2 Most famously, the South African Truth and Reconciliation Commission employed its own unique blend of Christian and African social anthropologies, symbolized in its use of the Bantu word " ubuntu ," 3 in an attempt to bring peace and healing to a nation that had been torn apart by centuries of racial oppression.Both secular and religious institutions have adopted restorative justice principles.4 Catholic bodies that have done so include, for example, many dioceses and 1 The literature on restorative justice is voluminous, but important texts include Howard Zehr, Changing Lenses: A New Focus for Crime and Justice (Scottdale, PA
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.079 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".