Determining a Fair Price for Carriage?: Applying a “Fee-Driven” Factor and Reverse Auctions to Adjudicating Carriage Motions in Ontario
Bibliographic record
Abstract
ABSTRACT: Since the introduction of the Class Proceedings Act, 1992, carriage motions have been assessed on the basis of an indeterminate and multi-factor test that provides a wide degree of discretion for judges. This has resulted in inconsistent outcomes that have not promoted the test’s consistency with the three objectives of class actions. As noted in Chu v Parwell Investments, however, one potential remedy is to place greater emphasis on fee arrangements in the form of a determinative “reverse auction.” This paper proposes that courts in Ontario should seek to formalize the goals encompassed within a new “fee-driven” factor that emphasizes the court’s use of a reverse auction. Judicial oversight is one differentiating factor between how reverse auctions have been previously applied in the United States and how this concept is being considered in Ontario. The paper proposes that Ontarian courts should cultivate this culture of judicial oversight to ensure that any future application of a reverse auction substantively contributes towards all three objectives of class actions. This would allow the court to passively structure the fee-driven factor towards providing economic savings for the plaintiff class and incentivizing both class counsel and third party funders to structure more equitable funding practices. This fee-driven factor is intended to compliment the multi-factor test and may be introduced via incremental progress in jurisprudence. Consequently, the paper’s proposal provides pragmatic options that easily integrate with the Law Commission of Ontario’s pre-existing recommendations and recent amendments to the CPA introduced by the Smarter and Stronger Justice Act.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".