Taking Responsibility for Intergenerational Harms: Indian Residential Schools Reparations in Canada
Bibliographic record
Abstract
From 2009 to 2012 the author lived and worked in Whitehorse as a lawyer for Justice Canada. One of her responsibilities was to attend Independent Assessment Process hearings in the role of “Canada’s Representative.” The experience of hearing from survivors and working within the limits of a torts-based process sent the author on an exploration of how harms are classified and remedied in Canadian law. The disconnect she felt between the narrow parameters of the legal process and the ongoing effects of historic harms that were evident in many aspects of northern life needed to be reconciled. Building on previous work that identified and classified harms, the author reviews the thirteen reparations that have been provided for the harms caused by the Indian Residential Schools policy in order to assess how well these reparations, when taken together, are able to address the full range of harms expressed by residential school survivors. The author then suggests additional mechanisms of responsibility, drawn largely from transitional justice theories, which could bring Canadians, as individuals and as a polity, into their role within the intergenerational legacy of the Indian Residential Schools policy and recognize the full range of harms experienced by survivors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".