Kerr v. Danier Leather: an Analysis of the Difficulty to Enforce a Duty to Update Statements about the Future in the Context of Securities Regulation
Bibliographic record
Abstract
Forecasts, predictions and opinions about the future should not be treated in the same\nway as hard information is treated under the Securities Act. Because this type of soft\ninformation cannot be verified in advance, the imposition of liability in respect of these statements about the future may hinder their production and have a result that is adverse to the interests of investors – who would prefer to hear management speak candidly about its thoughts on the company’s future performance. This essay examines the way in which the Ontario Securities Act treats statements about the future, as well as the most important decision in this area up to the present: Kerr v. Danier Leather. It will also discuss whether there should be a duty to update predictions when the circumstances that formed the basis of these forecasts have changed significantly.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".