Optimal Regulatory Areas for Securities Disclosure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The corporate governance scandals of 2003 have brought renewed focus on mandatory disclosure. One of the most fundamental questions relating to this kind of regulation is the choice of regulatory area. The United States initially faced this question in the 1930s when, after intense debate, it decided to move from an exclusively state-based system to one primarily relying on federal regulation. It is a hot issue today as well. The countries of Europe, for example, are currently deciding the extent to which the European Community, rather than its member states, should determine securities disclosure in Europe. Canada is deciding whether to follow the path taken by Australia in the 1980s and to enlarge the role of the federal government in a system that has traditionally left disclosure regulation primarily to the provinces. Also, advocates of issuer choice are urging the United States to reconsider its 1930s decision and to give issuers the option to choose among a reinvigorated set of state regulatory regimes. The issuer-choice school of thought is influencing the debates in Europe and Canada as well. Finally, other advocates are pushing for a move in the opposite direction, arguing that standards for issuer disclosure should be set at a global level.\nThis Article constructs an economic-efficiency based theory of optimal regulatory areas for securities disclosure. While larger political and constitutional considerations unrelated to efficiency will inevitably also play a role in the resolution of the debates recounted above, efficiency considerations are important because they go to the capacity of capital markets to promote the generation of real wealth. The theory developed here can help identify the efficiency-related tradeoffs involved in choosing one level of government versus another and the information that is needed to choose intelligently.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it