The role of reparative justice in responding to the legacy of Indian Residential Schools
Bibliographic record
Abstract
The Indian Residential Schools Settlement Agreement (IRSSA) is historic in nature and ambitious in its goals. With an estimated value of approximately $5 billion dollars, it aimed not just to settle the massive volume of litigation that the IRS legacy suddenly generated in the late 1990s and early 2000s but also to achieve a ‘fair, comprehensive and lasting resolution’ to the grievous, large-scale historic wrongdoing associated with Indian Residential Schools (IRS). In order to achieve this, the IRSSA established a number of innovative remedies and institutions. This paper seeks to illuminate those lesser-known but vitally important parts of the IRSSA that have compensation as their aim. As outlined here, those institutions can only be understood against the backdrop of the civil litigation system that gave rise to them. The civil justice system of the 1990s showed rather surprising willingness to dismantling some of the key obstacles that prevented IRS cases from getting to court. Nonetheless, the civil justice system retained many critical shortcomings that would have proven fatal for IRS plaintiffs. The compensatory elements of the IRSSA, such as the Common Experience Payment (CEP) and the Independent Assessment Process (IAP), were designed precisely to respond to and correct for these shortcomings. As such, they are an extremely important complement to the more well-known elements of the IRSSA like the Truth and Reconciliation Commission. This paper suggests that achievement of the overall goal of the IRSSA will depend in a very important way on the success of the reparative elements in achieving their compensatory goals.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.057 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".