Bibliographic record
Abstract
This chapter considers reparations for mass human rights abuses against Indigenous peoples, and essential elements for reconciliation. The chapter examines this topic from the experience of the writer as a primary negotiator in the settling of the historic Indian Residential School Settlement Agreement. The chapter argues that the largest and most comprehensive settlement in Canadian history for 150,000 claimants and over a period of 150 years could not have been accomplished through normal legal processes, either in courts or in traditional settlement negotiations. The chapter further argues that the formal justice system and the lawyers that operate within it do not have the capacity, values or training to deal with the mass harms perpetrated by state violations of human rights against Indigenous peoples. The chapter concludes by arguing that in post-colonial societies, traditional social, cultural and philosophical orientations to injuries and reparations must be rethought to allow for Indigenous perspectives and engagement. Legal processes, institutions and professional education must change.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".