Whither Common Law Claims for Secondary Market Misrepresentation?: An Analysis of Certification Decisions in <i>McCann V CP Ships, Silver V Imax, Mckenna V Gammon Gold, and Dobbie V Arctic Glacier</i>
Bibliographic record
Abstract
Part XXIII.1 of the Ontario Securities Act provides a statutory right of action for misrepresentations or omissions affecting the price of securities on the secondary market. Recent decisions have reached starkly contradictory conclusions to the question of whether common law misrepresentation claims — meaning claims that do not rely on the statutory right of action — may also be certified along with misrepresentation claims commenced pursuant to Part XXIII.1. These decisions provoke a reflection on the original reasons for the adoption of Part XXIII.1 and raise important questions about the past and future of the common law misrepresentation claims that Part XXIII.1 was intended to supplement, if not entirely supersede. The authors contend that Strathy J’s reasons for declining certification of the common law misrepresentation claims in McKenna v Gammon Gold are consistent with Canadian jurisprudence before the enactment of Part XXIII.1 and should be preferred to the reasoning in Silver v IMAX, McCann v CP Ships and Dobbie v Arctic Glacier.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".