THE “ILLUSION OF COMPENSATION”: CY PRÈS DISTRIBUTIONS IN CANADIAN CLASS ACTIONS
Bibliographic record
Abstract
In both the U.S. and Canada, the now common use of cy près in the design of class action settlement distribution plans represents a radical transformation of the original cy près doctrine. Despite the facilitative role of class actions in aggregating claims, in some cases there may be no practical way to calculate or pay hundreds of thousands of small claims. In its current manifestation in class actions, cy près has become the mechanism by which aggregation of loss is effected. Cy près is therefore used not only to dispose of unclaimed settlement funds, but to avoid having class members claim a portion of the settlement at all. In this way, cy pres creates the "illusion of class of compensation” (to borrow Martin Redish's term), because the bulk of the class receives no compensation at all. In this paper I critically and empirically examine the use of cy près in Canadian class actions, with references to developments in American cy près jurisprudence. First, I describe the origins and purpose of cy près in Canadian law. In Part II, I provide a taxonomy of the ways in which cy près has been used in class actions, and refer to illustrations of these categories in the case law. In Part III, I summarize and critique the various judicial approaches to cy près in Canadian class actions. In Part IV, I provide an empirical account of the use of cy près in Canada over the last two decades. It is, I believe, the most comprehensive collection of information regarding the nature and extent of cy près use in Canada. Finally, in Part V, I examine the policy questions surrounding cy près settlements identified in Redish's 2010 article, and conclude with observations about the use of cy près as it has developed in Canadian class actions.
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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 | 0.009 | 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 | 0.020 | 0.022 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".