Using Class Actions to Redress Historical Wrongs Committed by the Government
Bibliographic record
Abstract
The author argues that in many circumstances class actions can be an effective tool to redress historical wrongs committed by the Canadian government. First, a historical wrong’s suitability to be brought as a class proceeding must be assessed on the basis of whether the action meets the stated aims of judicial economy, access to justice, and behaviour modification, as well as through an analysis of other pragmatic issues. Like the majority of class actions, those for historical wrongs generally result in a settlement. Next, an examination of these settlements reveals that they can be understood to offer appropriate redress for historical wrongs when viewed through a reconciliatory, not a restorative, lens. Rather than claiming that monetary compensation restores the status quo, the reconciliatory perspective of class action settlements, exemplified by the Indian Residential School Settlement Agreement, views them as a means to establish trust and respect between perpetrator and victim. Finally, class actions also play an important role where the government is reluctant or unwilling to settle. In this situation, a class action provides victims with the best hope of redress. Essentially, class actions provide marginalized groups that have suffered historical wrongs with an instrumental and often necessary tool to seek compensation from the government.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".