Safety in Numbers or Lost in the Crowd? Litigation of Mass Claims and Access to Justice in Ontario
Bibliographic record
Abstract
Ontario’s Class Proceedings Act [CPA] is 30 years old. In the past three decades, it has inspired similar legislation across Canada and around the world, and its capacity for bringing about social change has been widely acknowledged. But, like all things that mature, some cracks are beginning to show. The certification test under section 5 of the CPA has been made more restrictive by recent legislative amendments. In addition, class action practitioners are starting to recognize that the CPA can be a blunt instrument and that some mass claims are better litigated outside of that context. While smaller claims may find safety in numbers in a class action, larger claims that require more individualized treatment may get lost in the crowd. Outside of the CPA, however, there is minimal guidance in this area, and this can lead to uncertainty and delay. This article proposes a set of informal guidelines for the litigation of mass claims in Ontario, informed by multidistrict litigation in the US and group litigation in England & Wales, as well as the theory and history of mass claims typology. This guidance will reduce uncertainty and delay by facilitating agreement between parties on procedural steps and provide much-needed direction for a growing phenomenon.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".