Bibliographic record
Abstract
Since it was issued two decades ago, the Ontario Law Reform Commission’s Report on Class Actions has been the foundational document for any discussion of the Canadian class action. Judges and commentators regularly cite the three goals or benefits of class actions that were outlined in that report, namely, improved judicial economy, behaviour modification, and improved access to justice. However, the Report necessarily relied largely on data from foreign jurisdictions to support its findings, and virtually no studies have tested whether or not these goals are being met by the Canadian class action. The author undertakes a critical analysis of the goals as stated by the Report on Class Actions and uncovers areas of conceptual and potential practical weakness. This analysis suggests some viable starting points for future studies of class actions that can validate and improve their usefulness in Canadian society. Ultimately, the author concludes that the continued assumption by the courts that these three goals are being forwarded may be correct, but is not justified by existing data. The fact that many valid criticisms have been raised suggests that studies of Canadian class actions and their results should be undertaken to ensure that the class action maintains a useful and beneficial role in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.104 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".