Our Aging <i>CPA</i> : It’s Time for Ontario to “Opt-In” to a Modern Global Class-Actions Framework
Bibliographic record
Abstract
Abstract: Notwithstanding the fact that “international” is the new standard for many businesses, Canadian courts continue to struggle with how to properly assert jurisdiction over claimants in an international class action. The current legislative framework provides no meaningful guidance, and the current test was not designed to handle the unique jurisdictional issues of a global class. The result is an unpredictable, unstable body of caselaw that flies in the face of the constitutional principles of order, fairness, and comity. It is time for legislative intervention. I suggest that the most appropriate model would be a hybrid regime that requires domestic claimants to opt-out, but foreign (that is, non-Canadian) claimants to opt-in. A hybrid model will also ensure that Canada’s jurisdictional rules pertaining to foreign claimants are in line with established internationally accepted norms, and bring Ontario’s regime up to date with recent developments in the European Union and the United Kingdom in anticipation of the expanding role of class action and collective settlement regimes beyond the bounds of North America. It would also provide greater protections to Canadians where other countries, such as the United States, presume to include Canadians in foreign opt-out proceedings without the consent of the Canadian class members. In short, a hybrid model would introduce a welcome level of clarity into the jurisdictional analysis of a global class.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".