Questions About Leave Test for Secondary Market Claims Remain Unanswered After Court of Appeal Decision in <i>Rahimi V Southgobi Resources Ltd</i>
Bibliographic record
Abstract
Abstract: This article comments on the Ontario Court of Appeal’s recent decision in Rahimi v SouthGobi Resources Ltd in which the court reversed a decision denying leave under section 138.8(1) of the Ontario Securities Act against individual defendants who were former executives and directors of the issuer based on the reasonable investigation defence. I argue that the practical and legal issues that the Supreme Court failed to adequately address in Theratechnologies Inc v 121851 Canada Inc and Canadian Imperial Bank of Commerce v Green remain, and are perhaps even murkier, as a result of the Court of Appeal’s decision in Rahimi. In particular, the court did not adhere to the threshold for leave established in Theratechnologies and elided discussion of the standard of appellate review and the forensic powers available to judges hearing leave motions under Part XIII.1. I argue that the litigants, the bar, and lower courts would benefit from more direct and definitive guidance on these aspects of the leave test for secondary market securities claims.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".