Correcting Corrections: Resolving Confusion Over the Public Correction Requirement in the Ontario Securities Act
Bibliographic record
Abstract
To bring a class action under Part XXIII.1 of Ontario’s Securities Act (civil liability for secondary market disclosure), plaintiffs must show that there was a public correction. Two judges have split on what this means in practice. Justice Perell has interpreted the requirement broadly, as calling for both semantic and statistical evidence to establish the claim. Justice Belobaba has interpreted the requirement narrowly, as calling for limited semantic analysis and, generally, no statistical evidence. This article seeks to reconcile these two approaches. It argues that the best interpretation of the language of both judges’ decisions points towards the limited approach. It further argues that the limited approach is more consistent with legislative intent, avoids procedural confusion, and produces the best incentives for all capital market participants. In coming to these conclusions, this article also explains how the statutory language relating to public correction can be applied in the context of multiple partial corrections. This feeds into the conclusion, which provides some brief guidance to litigants on pleading, particulars, evidence, and calculating damages in Part XXIII.1 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 | 0.033 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.012 |
| 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".