Tools for Regulatory Quality and Financial Sector Regulation
Bibliographic record
Abstract
This report provides a comparative perspective on the application of quality regulation principles to financial sector regulators, in the US, Canada, Australia, the UK and France. The report compares key provisions of the codes of the Basle Committee and IOSCO, with the OECD's 2005 Guiding Principles for Regulatory Quality and Performance, and the 2009 Policy Framework for Effective and Efficient Financial Regulation (PFEEFR). The report analyses the independence and accountability of the regulators, as well as their powers. The analysis focuses on requirements for ex ante and ex post regulatory impact analyses, including burden reduction; for transparency and communication of decision making, as well as co-ordination and regulatory review; for improving the regulatory system over time and for regulating conflicts of interest. The report finds variation in the formal arrangements, and respective practices. It also finds that the requirements related to better regulation principles are often implemented too late in the decision-making process when regulations are set at the international level.
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 | 0.100 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".