Bibliographic record
Abstract
This essay focuses on the public forms of financing class litigation, and argues that financing class actions publicly through assistance by entities such as the Canadian province of Quebec's Fonds d'aide aux recours collectifs (the assistance fund for class action lawsuits; the Fonds) is a most appropriate and effective way to finance class action litigation, whenever available. I develop the proposition that the Fonds entity is not only effective as a class litigation funding mechanism, but also as a mandatory independent oversight body beneficial to the class action system and the industry as a whole, and that it should be recognized as such and serve as a model for reform of other legal systems. I argue that for the objectives and public policy purposes of class actions to be fulfilled, successful cases must be used to help finance unsuccessful ones. Assistance must be provided to legitimate and promising cases from entities with proper motivations: that is, to provide a way to fund this kind of litigation, to provide true access to justice. Because the Fonds' right to compensation applies to all class actions in Quebec, every class action case initiated in the province-whether it is funded or not-helps finance the next one. Furthermore, the Fonds' motive to assist class plaintiffs in a neutral manner helps provide access to worthwhile cases. As such, the very structure and functioning of Quebec's public class action assistance fund immunizes it from potential conflicts of interest and salves the risk of agency cost in representative actions.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".