Judicial Scrutiny of Third Party Litigation Funding Agreements in Canadian Class Actions
Bibliographic record
Abstract
Abstract: The emergence of third party litigation funding agreements — contracts by third parties funding litigation in which they have no direct stake or claim, usually in exchange for a percentage of the recovery — has animated vigorous debate about some of the most fundamental issues in the legal system. Some observers have pegged renewed hope on these agreements to increase access to the courts, economize judicial resources, and punish wrongdoers, while others voice concern over their potential to result in undue manipulation of litigation, frivolous lawsuits, subordination of control to rapacious investors, commodification of claims, and other negative social consequences. As these debates rage, the phenomenon has been left to judges to regulate. This paper examines recent trends in third party litigation funding of class proceedings in Canada and analyzes the efforts by Canadian judges to bring scrutiny to such funding arrangements. The paper argues that these efforts generally succeed in balancing the concerns of proponents and opponents of such agreements.
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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 | 0.031 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 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".