Resolving Mass Legal Disputes Through Class Arbitration: The United States and Canada Compared
Bibliographic record
Abstract
RESOLVING MASS LEGAL DISPUTESbenefit from a deeper understanding of the differences in the way in which the United States and Canada address the tensions between collective redress and arbitration.This Article undertakes just such a comparative analysis and proceeds as follows.First, Section II lays the groundwork for comparing class arbitration in Canada and the United States by describing relevant aspects of each nation's legal system.Section III then introduces the concept of class arbitration, including its basic procedures, its history and its importance in both domestic and international dispute resolution.Once the foundation has been laid, the comparative analysis begins.Section IV contrasts the current state of class arbitration in the United States and Canada, focusing on three issues that have arisen as a result of recent Supreme Court precedent in both countries and that are particularly amenable to comparative analysis: circumstances in which class arbitration is available; procedures that must or may be used; and the nature of the right to proceed as a class.Section V concludes the Article by bringing the various threads of analysis together and identifying the lessons that can be learned from comparing the two countries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".