Reconciling Tort and Administrative Law Concepts of Justice: The Case of Historical Wrongs
Bibliographic record
Abstract
The first part of this article provides an overview of the most dominant private and public law approaches that have been attempted in the courts by plaintiffs seeking redress for historical wrongs and outlines why these approaches have been unsuccessful. It also defines the notion of historical wrongs and provides background on the two historical wrongs used as a case study in this paper – Aboriginal residential schools and sexual sterilization in Alberta. In the second part, I turn to discuss the phenomenon of creating compensation schemes as an alternative to traditional court action. Two illustrative examples are the outcry surrounding the introduction of a statute to compensate the victims of sterilization in Alberta and the continuing challenges related to the Aboriginal school resolution process established by the federal government. An examination of the compensation schemes that emerged in these two contexts as well as the process of their emergence provide valuable insight into some of the tensions that can occur when systems of compensation for victims of historical wrongs are designed. I argue that these tensions may be addressed by fostering continuous dialogue between the government and the victims and through independent oversight. Finally, I offer some observations on the ways in which compensatory schemes for historical wrongs expand our traditional conceptions of administrative justice.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.144 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".