A CASE FOR FEDERAL SECURITIES REGULATION
Bibliographic record
Abstract
Attached is a copy of a paper I recently finished in connection with my Masters of Securities Law at Osgoode Hall Law School. In it, I argue for a federally constituted securities regulator responsible for regulating interprovincial and international securities matters to replace the current structure. Provincial regulators will continue (at their option) to regulate purely intraprovincial transactions, which, it is speculated, will comprise a minimal proportion of capital markets activity. I wish to submit the paper to the WPC not only to promote the regulatory model proposed in the paper, but also because it discusses many of the topics specified in the WPC's "Questions to the Canadian Capital Markets Community" issued in May. Specifically, the following issues are addressed in the paper: • strengths and weaknesses of the current structure (including reference to regulatory costs, loss of international competitiveness and enforcement issues)- pgs 2 to 16; • analysis of regulatory structures in other countries (Australia, E.U. and U.S.)- pgs 49 to 56; and • optimal regulatory structure- pgs 58 to 66. The paper also includes an assessment of other regulatory models, and how they may compare to the federal model ultimately proposed. I hope this paper adds to the debate. I certainly enjoyed thinking the issues through and articulating a
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".