De kloof overbruggen met herstelrechtelijke reacties op misstanden in organisaties: lessen uit het proces van overgangsgerechtigheid op de Canadese kostscholen
Bibliographic record
Abstract
Bridging the gap with restorative justice responses to abuses in organisations: lessons from the Canadian Residential School’s transitional justice process Restorative justice and transitional justice are increasingly recognized as complementary approaches. While they have traditionally operated in different domains, restorative justice at the interpersonal and transitional justice in post-conflict or large-scale human rights violation contexts, their integration offers promising pathways for more holistic justice processes. This article contributes to the broader debate on their synergies by focusing specifically on the context of abuses in organisations. These abuses often reflect systemic and enduring patterns of harm that affect large numbers of victims, requiring justice responses that address not only micro-level interpersonal healing but also macro-level structural transformation. This makes it a particularly interesting context for examining how restorative and transitional justice can intersect. Using the Canadian case of residential schools as an example, the article explores how transitional justice practices and frameworks such as acknowledgement, truth-seeking, (symbolic) reparations, and guarantees of non-repetition, can deepen and extend restorative justice practices in institutional settings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".