Centralized or Competitive Securities Regulation: What Canada can learn from the US and the EU Jean-Marc Suret*
Bibliographic record
Abstract
The promoters of the uniformity and centralization of securities regulation in Canada argue that a single authority and a perfectly homogeneous regulation would be preferable to the current situation. This paper summarizes the evidence and lessons from the U.S. and the E.U.’s experience in this field. We study the application of the concept of regulatory competition in the United States in the area of company law and securities law. We analyse the steps taken by the European Community to develop a mutual recognition system--the European passport. We conclude with the lessons learned from these initiatives and thoughts on the debate presently taking place in the Canadian securities industry. 2 Centralized or Competitive Securities Regulation: What Canada can learn from the US and the EU Arguments put forth by promoters of the uniformity and centralization of securities regulation in Canada rely essentially on the concept of the immediate effectiveness of such regulation: a single authority would be able to regulate the securities field in an
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".