Class Actions in Canada: Cases, Notes, and Materials, 3rd Edition
Bibliographic record
Abstract
Class Actions in Canada: Cases, Notes, and Materials, 3rd Edition is the definitive resource for a comprehensive understanding of class actions in Canada. Authored by esteemed experts from across the country, this essential text examines leading-edge case law and current legislative regimes that shape the certification process, representation, and settlement approval. From the fundamental stages of the litigation process to the complexities of specific claims, including consumer protection, product liability, privacy, environmental, securities, competition, and employment, this resource covers a range of issues that arise in class actions. The new edition sheds light on the latest trends shaping this dynamic area of law, such as the release of the Ontario Law Commission Report on Class Actions, the adoption of the CBA Protocol for the Management of MultiJurisdictional Class Actions, and the emerging influence of private third-party funders. With its comprehensive coverage, updated information on legislative reforms, and examination of key cases, this text is an indispensable resource for law students, practitioners, and academics studying or working with class actions in Canada. This casebook was generously sponsored by Davies Ward Philips & Vineberg, Fasken Martineau, Koskie Minsky LLP, McCarthy Tétrault, Osler Hoskin & Harcourt, Rochon Genova LLP, and Torys LLP.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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".