The Doctrine of Cy Près in Ontario Class Actions: Toward a Consistent, Principled, and Transparent Approach
Bibliographic record
Abstract
This essay explores the contentious aspects of the use of the legislated cy près doctrine in Ontario class actions and provides recommendations on how to improve the consistency, transparency, and overall fairness of cy près distributions. The focus of this essay is a series of five suggested approaches that the court can adopt when fashioning or approving the cy près application of funds in whole or partial judgments and settlements. These alternative schemes provide methods to aid the court in deciding exactly who should be the recipient of cy près funds and why such a beneficiary should receive cy près funds. The suggested approaches emphasize the importance of establishing a nexus between the cy près recipient and the underlying cause of action, as well as providing an indirect benefit to class members. Throughout the essay, the contentious aspects of Ontario’s use of the cy près doctrine and potential alternatives to its current use are illustrated through a comparative analysis of the use of cy près in the United States. This essay culminates in a final recommendation to Ontario courts, proposing an optimal approach to choosing the most appropriate cy près recipients, as well how to convey this choice effectively in judgment. The overall analysis considers the various approaches with reference to class action objectives: judicial economy, access to justice, and behaviour modification.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".