LE RECOUVREMENT ET L’INDEMNISATION DES MEMBRES DANS L’ACTION COLLECTIVE
Bibliographic record
Abstract
This article aims to analyze, the process that leads to the recovery and distribution of amounts awarded to class action members, in accordance with the relevant sections of the new Code of Civil Procedure that came into effect in 2016. The first part of the article presents the basic assumption that class actions are first and foremost a means of compensating members, but that this primary objective is only imperfectly attained. Then, in the second part, the assumption is compared to the recovery regime provided for in the Code, in light of the results of an empirical study of class action cases conducted by the Class Actions Lab at the Universite de Montreal in the summer of 2015. That part also addresses questions pertaining to evidence, and involvement of the courts in recovery and the determination of amounts to be remitted to the Class Action Assistance Fund. In particular, the following issues are discussed: How do you define the injury that is common to members, assess it in light of disparity in injuries suffered and, lastly, manage to adequately compensate members of the class action? Should damages for members be ensured in a different way, under a new vision? What can be said in this respect about the positive effect felt by members who feel there is a dissuasive, behaviour-modifying effect? In the third part, the collective recovery procedure and its forms of direct and indirect liquidation are addressed, with emphasis on its advantages and terms and conditions. The article concludes with comments in favour of using class actions to achieve the objectives of dissuasion and member compensation.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".