Against the churn: institutionalization, transformation, and restorative justice
Bibliographic record
Abstract
Drawing on twenty interviews conducted in 2022 with restorative justice practitioners and transformative justice advocates in Winnipeg, Manitoba, we examine how restorative justice navigates the churn of institutionalization. In Manitoba, this churn is strengthened through the creation of the Restorative Justice Centre as a government clearinghouse for referrals and funding. Simultaneously, anti-institutional philosophies of defunding, abolition, and decolonization have seen recent increased uptake among segments of the population. Based on our interviews, we argue that restorative programming in Manitoba has institutionalized to a degree that there is little optimism toward aligning or synthesizing restorative and transformative justice. But this does not mean each must remain its own solitude. Focusing on restorative justice and the threat of further institutionalization, we suggest restorative practitioners anchor themselves to transformative ideals, while also using transformative justice as a horizon by which they might seek to correct course when the pull of institutionalization becomes increasingly strong.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.039 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".