Unsettling the lawyers: Other forms of justice in Indigenous claims of expropriation, abuse, and injustice
Bibliographic record
Abstract
This article considers, from the experience of the Indian Residential Schools Settlement, the limitations of the current formal justice system and the common ways that lawyers and parties act within it. Looking at the combinations of lawsuits, settlement negotiations, structured compensation schemes, truth and reconciliation processes, and memorial and education programs now provided for in the IRSS, the article suggests that we may need ‘process pluralism’ and different orientations to deal with modern mass harms: now recognized harms (like loss of culture, family, language, as well as physical, mental, and social injury) that the formal legal system has not yet developed the capacity to address. Placing the IRSS in a larger international context, the article suggests that some legal and social recognition of ‘new’ human harms and injuries has necessitated the development of different legal and quasi-legal processes. Whether called ‘restorative,’ ‘transitional,’ or ‘alternative’ justice, new forms of dealing with wrongs, harms, and conflicts will require redesigning legal processes and institutions; legal professional education; and social, cultural, and philosophical orientations to human injuries and ‘redress.’ Not all who are injured (both individually and in groups) want or require the same ‘remedies,’ and our conventional and historical common law and adversarial system must be adapted to the diverse needs of those who are injured by past and unconscionable wrongs, especially when inflicted by major governmental, religious, and civil society institutions and practices.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.113 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".