The Emergence of a New Battleground: Liability for Secondary Market Violations in Ontario
Bibliographic record
Abstract
During the past decade, Canada has seen a dramatic rise in securities class action lawsuits. The vast majority of these lawsuits have been filed in Ontario, the location of Canada's principal public securities market, the Toronto Stock Exchange (TSX). The most likely explanation for this development is the enactment of section 138 of the Ontario Securities Act in 2005, otherwise known as Bill 198.\nIn this article, the authors provide illustrative data from Ontario to evidence the recent growth of securities class action filings. The article discusses the section 138 amendments, including such provisions as secondary market liability, certification of class status, and the monetary ceiling on liability. Also, the article examines some key differences between the Ontario Securities Act and the United States Securities Exchange Act.\nThe authors conclude that because of the plaintiff-friendly provisions found in the Ontario Securities Act, combined with favorable court decisions, U.S. investors will likely seek to litigate future claims that have a sufficient nexus to Canada in the Canadian courts. Moreover, court decisions that certify a global class will shape future litigation in Canada. Due to these developments securities class actions in Canada (and particularly Ontario) should remain vibrant and may well provide a forum for adequate redress for non-Canadian investors in appropriate cases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".