Bibliographic record
Abstract
The Ontario government has recently made changes to provincial securities law that are aimed at more effective enforcement. For example, statutory civil remedies are now available to investors in actions involving misrepresentation or inadequate disclosure in the secondary market. A broader range of sanctioning options has also been made available to the Ontario Securities Commission. The author explores the factors contributing to these developments, identifies recent controversies surrounding the Commission's enforcement activities, and evaluates the effectiveness of different approaches to enforcement. The author reviews policy issues surrounding enforcement through public, criminal and quasi-criminal sanctions, as well as civil remedies, and places these issues in the context of academic legal debate. She considers administrative law principles in the context of issues in securities enforcement, such as apprehension of bias, “judicialization" of Commission hearings, and the diversity of enforcement efforts across Canada. She then considers whether regulations should be oriented to deterring violations or creating incentives for compliance. She notes that current incentives to comply with securities regulations may have little influence on employee and firm behaviour in a competitive business environment. Enforcement mechanisms aimed at deterrence may therefore be less effective than those seeking to encourage compliance with regulations. Since the provisions recently added to the Ontario Securities Act are aimed at deterrence rather than compensation, she then discusses whether private enforcement mechanisms, such as the statutory civil remedies available under that Act, are preferable to public enforcement mechanisms. The author concludes that public and private mechanisms may be interdependent and could together achieve effective securities regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".