Bibliographic record
Abstract
Stock exchanges around the world have recently discarded their traditional mutual membership structure in favor of a for-profit corporate format. This development increased fears of conflicts of interest, as for-profit exchanges are more sensitive to pressures from their constituents and more likely to abuse their regulatory powers. In this Article, we explore the allocation of regulatory responsibilities to market infrastructure institutions, administrative agencies, and central government entities in the eight most influential jurisdictions for securities regulation in the world. Examining how different jurisdictions answer this question is particularly pressing given the December 2006 transatlantic stock exchange merger activity. After discussing the role of self-regulatory organizations in the oversight of modern stock exchanges, we report the results of a survey of the allocation of regulatory powers in a sample of eight key jurisdictions. In that survey, we examine the allocation of such powers at three levels: rulemaking, monitoring of compliance with these rules, and enforcement of rules violations. Based on our findings, we categorize these jurisdictions in three distinct models of allocation of regulatory powers: a Government-led Model that preserves significant authority for central government control over securities markets regulation, albeit with a relatively limited enforcement apparatus (France, Germany, and Japan); a Flexibility Model that grants significant leeway to market participants in performing their regulatory obligations, but relies on government agencies to set general policies and maintain some enforcement capacity (United Kingdom, Hong Kong, and Australia); and a Cooperation Model that assigns a broad range of power to market participants in almost all aspects of securities regulation, but also maintains strong and overlapping oversight of market activity through well-endowed governmental agencies with more robust enforcement traditions (United States and Canada).
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".