The Pre-Authorization Disclosure of Legal Grounds: Why Recognition of the Right to Legal Particulars is Appropriate and Needed in the Quebec Class Action Context
Bibliographic record
Abstract
Unlike in other jurisdictions, Quebec petitioners often do not set out the theories of liability they intend to invoke. Rather than specifying the codified, statutory, or regulatory principles and provisions on which their proposed class action is based, such petitioners generally content themselves with allegations like “the suit for which authorization is sought is an action in damages.” In this article, the authors explain that statements of this type are insufficient and that it is often in the interests of everyone, including the class members, that the true nature and scope of the proceeding be understood. Accordingly, when petitioners fail to state their case with appropriate precision, motions to obtain legal particulars should be granted.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".