Price-fixing Class Actions: A Canadian Perspective
Bibliographic record
Abstract
Prior to the enactment of class proceedings legislation in Canada, there were virtually no price-fixing cases commenced.Prior to 1993, when Ontario's Class Proceedings Act' was proclaimed into force, the complexity and expense associated with pursuing pricefixing litigation had rendered the justice system in Canada largely inaccessible to all but a few select persons harmed by the conspiracies.Without specific class proceedings legislation, it is the authors' belief that Canadians would, like others around the world, have to rely on the U.S. courts to attempt to obtain recourse.Fortunately, Canadian courts have demonstrated that they have the ability to effectively provide justice for their citizens.In Canada, class proceedings provide those victimized by price-fixing conspiracies access to justice in an efficient manner.And, in 1999, the Siskinds firm 2 filed the first of many class actions that focused on price-fixing cartels and the harm they caused to Canadians. 3 Recent attempts to have the rights of international claimants adjudicated in U.S. courts have the potential to provide an alternative and likely beneficial route for Canadians.Given Canada's unique situation, however, parallel proceedings with increased cooperation may be more appropriate.In the meantime, defendants seeking to *
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.033 | 0.022 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 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".