Two faces of transitional justice: Theorizing the incommensurability of transitional justice and decolonization in Canada
Bibliographic record
Abstract
Transitional justice is a complex form of political and legal intervention used by state governments to redress state-sanctioned and large-scale harms (Balint, Evans, & McMillan, 2014). The typical aims of this model include maintaining peace during times of political flux, installing rule-of law, creating new historical narratives, and reconciliation (Teitel, 2003). In both theory and practice, transitional justice usually concerns ‘fragile states’ or post-conflict states. Governments, academics and practitioners, however, are broadening transitional justice theory and practice to include harms to Indigenous peoples in settler states such as Canada. Notably, recent efforts seek to integrate ‘decolonization’ into transitional justice as a desired process or goal. This paper is a critical intervention into this trend. I demonstrate that Canada has two faces of transitional justice – one, internally focused on ‘reconciliation’ with Indigenous peoples and the other, externally focused on providing peace and security expertise to fragile states. I bring land-centered understandings of decolonization and Indigenous resurgence into conversation with this duality to argue that efforts to incorporate decolonization into transitional justice, without taking seriously its roots and the international transitional justice work with which Canada is engaged, does more to obscure and de-legitimize Indigenous nationhood and settler colonialism entirely.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.046 | 0.056 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".