Class Actions and Beauty Pageants: The Need for Carriage Motion Reform in Ontario
Bibliographic record
Abstract
Abstract: In Ontario, carriage motions have been embraced as the designated method for determining which of competing class counsel will exclusively represent the class. Despite the foundational objective of judicial economy, carriage motions persist as inefficient and deeply flawed procedures. Since the initial factors for the court to consider were enumerated in Vitapharm, the test has grown into a lengthy list of factors which are neither mandatory nor exhaustive. This promotes uncertainty and does little to foster judicial economy or serve the best interests of the putative class. I argue that carriage motions have developed in an unfeasible direction. They encourage wasted resources and effort, create delay in an already lengthy process, and may ultimately prejudice the class. In search of an alternative, I employ a multijurisdictional analysis. The approach of the Federal Court of Australia in resolving competing actions is considered, as is the post-Schmidt approach of Quebec. Embracing the modified first-to-file rule of Quebec is the appropriate direction for carriage motion reform in Ontario. Quebec’s approach balances concerns over the complexity, uncertainty, and potential prejudice of carriage motions with concerns over the arbitrariness and unfairness of a conventional first-to-file rule. This would avoid the lengthy comparative analysis of carriage motions while still serving the best interests of class members.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".