How Class Actions have Shaped Litigation Financing Law in Canada
Bibliographic record
Abstract
Abstract: Third party litigation financing has become more widely accepted as a justifiable exception to the law against champerty for its potential to improve access to justice for plaintiffs with meritorious claims but who lack the funds to pursue their actions. In Canada, litigation funding was introduced as an accessory to class actions in part because of the statutorily mandated court supervision, and in part because funding agreements are often critical to advancing these actions when faced with large costs exposure. The disproportionate presence of class actions in Canadian jurisprudence on litigation financing has resulted in a unique system in which much of the analysis developed to evaluate the legality of litigation financing agreements in class actions has been applied beyond this context. The intent of this paper is to provide an overview of the current law of litigation financing and to canvass ethical issues that have received the most attention; namely, ensuring that funders are not overcompensated and do not interfere with the lawyer-client relationship. Although the law of litigation financing is still in its infancy, trends have emerged that allow the extrapolation of the future trajectory of the industry. This paper also considers some dichotomies that appear to be emerging, such as those between vulnerable and more sophisticated plaintiffs, between litigation financing and insurance, and between indemnification agreements and those that invest significantly more in the litigation.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.018 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".