The Short End of the Stick: Bolstering Legal Protections for Short Sellers in Ontario’s Secondary Market
Bibliographic record
Abstract
Abstract: In this paper, the author surveys Ontario’s secondary market civil liability framework. The author reviews the constituent continuous disclosure obligations as well as the enforcement mechanisms that are available under common law and statute. The author then explores how short sellers fit into Ontario’s secondary market securities laws. Avenues of legal recourse have seemingly crystallized for ordinary investors who are misled by reporting issuers in the secondary market. However, Ontario’s securities laws are unclear regarding the legal redress that is available to aggrieved short sellers who are resigned to a similar fate. To address this gap, the author argues in favour of strengthening legal protections for short sellers by: (1) recognizing a duty of care owed by public issuers to short sellers; and (2) revising the damage calculation formulas in Part XXIII.1 of Ontario’s Securities Act to ensure that they are capable of compensating short sellers in a manner that is commensurate with their investment position. In doing so, Ontario could better position itself as a robust securities market that provides adequate legal safeguards for diverse types of investors. Moreover, the implementation of remedial measures for short sellers may create market conditions that encourage the spread of negative information about stocks, paving the way for greater accuracy in the price discovery of shares and bolstered market efficiency overall.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".