Homegrown Transplants: Procedural Borrowing in the New Ontario Class Proceedings Act
Bibliographic record
Abstract
The most controversial of the recent amendments to Ontario’s Class Proceedings Act is the addition of two requirements to the certification test: to meet the preferable procedure criterion, s. 5(1.1) requires that common issues in the litigation must now “predominate” over individual issues, and a class action must be “superior” to all other forms of resolution. The importance of the interpretation of Ontario’s new certification test to the continued viability of class actions in the province merits a thorough and rigorous analysis of s. 5(1.1). The language of predominance and superiority is strikingly similar to requirements that have long applied to US class actions for monetary damages. As courts in Ontario begin to grapple with the new predominance and superiority requirements, however, the authors caution against turning to American jurisprudence for guidance. Several important structural differences between the Ontario and American class action regimes, as well as different constitutional considerations and a variety of approaches within US case law diminish its utility. Instead, the authors examine the history and language of the amendments to propose an interpretation of the predominance and superiority requirements that is informed by Canada’s own procedural and constitutional framework and that avoids the pitfalls of legal transplants.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".