On Lemons and Leather: Liability for Misrepresentations of Forward-Looking Information in Danier Leather
Bibliographic record
Abstract
The Supreme Court of Canada considered in Kerr v. Danier Leather the liability of a corporate issuer of a prospectus forecasting performance that was alleged to misrepresent the company’s prospects at the time purchasers bought shares. This article analyzes whether there is an obligation on issuers to update previously disclosed forward-looking information (FLI) whenever circumstances change, and whether the business judgment rule applies to such disclosure decisions. The author provides a policy analysis of whether the decision’s interpretation of the Ontario Securities Act best advances the societal interests underlying potential liability for misrepresentations on FLI. As a general rule, there is no obligation on an issuer to disclose FLI. Given that mandatory disclosure rules do not extend to FLI, and given that the issuer itself will predictably bear costs if its FLI disclosures lack credibility, the law should allow the issuer itself to customize the standard for evaluating its liability for misleading FLI. The article concludes that the Danier decision is correct in light of this policy analysis, as it leaves scope for issuers to tailor liability for FLI to their particular circumstances.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".