Rethinking the Approval of Class Counsel’s Fees in Ontario Class Actions
Bibliographic record
Abstract
Class action legislation is a relatively new phenomenon in several Canadian provinces. The state of the law, particularly in Ontario, is at a pivotal stage. We have a sufficient number of decided cases to draw some conclusions about how well the Class Proceedings Act, 1992 as it is currently being interpreted and applied is meeting its goals, but not so many decided cases that a settled approach has emerged to its application in the courts. In this article, I analyze a sample of twenty-seven reported Ontario class action decisions, focusing in particular on what the courts have done with respect to the approval of class counsel fees. I find that courts have by and large tended to use an enhanced "lodestar method" for compensating class counsel, whereby class counsel's base fee is adjusted with a multiplier to reflect the riskiness of the litigation. In the twenty-seven class actions I analyze, the average fee award per case is approximately $3 million, representing approximately 15 percent of the average settlement. The average multiplier is about 2.5. Under the current provisions of the CPA I argue that, given the incentives facing class counsel, a percentage contingency fee would be superior to the lodestar method, could more easily be monitored for abuses by judges, and would increase access to justice for potential claimants with independently non-viable claims.
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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 | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".