<i>Cy Pres</i> Awards in Canadian Class Actions: A Critical Interrogation of what is Meant By “As Near as Possible”
Bibliographic record
Abstract
Recently it has become apparent that a troubling trend is developing in the use of cy pres remedies in class actions cases. This paper offers a critical interrogation of what is meant by “as near as possible” in cy pres awards by exploring the use of settlement funds in Canadian class actions. Using a comparative analysis of the use of different types of cy pres awards and the extent to which they achieve the class actions objectives — judicial economy, access to justice, and behavior modification — this paper provides a comprehensive understanding of the doctrine’s use in class actions. Two alternative methods of implementing the doctrine, informed by a more thorough understanding of the use of cy pres in class actions, are presented. In conclusion, the authors acknowledge that no settlement is perfect, but this does not mean that the courts should sit idly by while such inappropriate cy pres settlements continue to be negotiated. Instead the authors encourage the courts to reconsider the recent, troubling trend and instead seriously consider the regulatory nature of class actions and the ways in which granting the award can serve a regulatory purpose, as well as the underlying objectives of class actions in general. All this is to ensure that cy pres can continue to offer a class actions remedy “as near as possible” to the ideal remedy.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".