دستیابی به قوانین مالی اصلح در چهارچوب گاتس: چارچوبی برای همکاری و رقابت در تدوین قوانین ذخیره قانونی بانک ها و افشاء اطلاعات در بازار بورس (Optimal Level of Financial Regulation Under the GATS: A Regulatory Competition and Cooperation Framework for Capital Adequacy and Disclosure of Information in Stock Market)
Bibliographic record
Abstract
Optimal level of regulation and regulatory reform seem to be an undisputed objective. However, in the context of financial regulation there are differences and disagreements over the optimal level of disclosure of information and capital adequacy standards. In this paper, instead of offering a substantive solution, we argue for a competitive process through which the optimal level of disclosure of information and capital adequacy standards are likely to emerge. The solution, which we suggest, consists of an international framework combining competition and cooperation among national regulatory regimes. We identify WTO/GATS as such a framework, which facilitates both regulatory competition and regulatory cooperation. The ultimate outcome of this integrated process, which simultaneously emphasizes liberalization and regulation of financial markets is the emergence of an optimal level of regulation related to mandatory disclosure of information and capital adequacy standards.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".