On Lemons and Leather: Liability for Misrepresentations of Forward-Looking Information under Ontario Securities Law
Bibliographic record
Abstract
INTRODUCTION• In a. recent landmark case, Kerr v. Danier Leather, 1 the Supreme Court of Canada considered the potential liability of a corporation that issued a prospectus containing a forecast of performance that was. at least arguably, misrepresenta tive of the company's prospects at the time purchasers bo'ugh t shares.The basic facts of the case are as follows.The shares of Danier Leather were sold in an lPO.The associated prospectus contained a forecast.Prior to the closing of the IPO, Danier's internal analysis revealed that unseasonably warm weather had dampened sales thus raising a question of whether the forecast in the prospectus would be met.Danier did not disclose that sales had been lagging and closed the !PO without an update to the forecast in the prospectus.A strong promotion resulted in the forecast being substantial1y met in the end.The trial judge found Danier liable for making a misrepresentation in its prospectus, and the Ontario Court of Appeal reversed.While not accepting all of the Court of Appeal's analysis, the Supre1ne Court upheld its finding and dismissed the appeal.The case has been controversial, as perhaps exemplified by the fact that three courts that considered the legal questions underlying the relevant dispute took three very different approaches to their resolution.The case raises two contentious questions: is there an obligation on issuers to update previously disclosed forward-looking information (ru) whenever circumstances change?And, does the 1.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".