“Cost-Shifting” and Access to Justice: A Quantitative Review of Certification Motion Cost Awards in Ontario
Bibliographic record
Abstract
Abstract: In the twenty-five years since Ontario’s Class Proceedings Act, 1992 has come into force, a number of concerns have been expressed by judges, practitioners, and academics regarding the success of Ontario’s “cost-shifting” regime. In particular, the significant adverse cost awards faced by would-be representative plaintiffs at the certification stage threaten to undermine the primary goal of class proceedings — access to justice. In response to a gap in the literature, and in light of the Law Commission of Ontario’s recent decision to undertake a review of class actions in Ontario, this paper provides a quantitative analysis of how cost decisions in the certification motion context have evolved throughout the life of the Class Proceedings Act, 1992 and focuses on whether the current costs regime is inhibiting access to justice. The data confirms that the quantum of cost awards has been increasing over time, especially during the last eight to ten years, and that the Class Proceedings Fund and section 31(1) of the Class Proceedings Act, 1992 have not consistently succeeded in protecting plaintiffs from the potentially negative impact of these rising costs. In light of these concerns, the paper concludes with recommendations that have the potential to help maintain the viability of the existing costs regime while preserving access to justice.
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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 | 0.036 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.065 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".