Convergence and Integration of Banking Sector Regulations in the Euro-Mediterranean area Trends and Challenges
Bibliographic record
Abstract
This analysis of regulatory convergence shows that substantial improvements have been made in the southern and eastern Mediterranean countries (SEMCs), yet they still suffer from key weaknesses in deposit insurance, entry obstacles, political interference and the strength of legal rights. In particular, deposit insurance systems in many SEMCs are not explicit, which could lead to uncertainties in the provision of support to banks in case of default. Moreover, most systems do not attempt to align the banks’ incentives in risk-taking with those of taxpayers by implementing risk-based premiums. Another persistent issue is the presence of entry obstacles, with signs of substantial barriers to entry and continued government ownership of banks. The comparison of regulatory systems also highlights that some SEMCs have barely been able to catch up with the strong increase in supervisory independence in EU Mediterranean countries in recent years. While creditor protection remains relatively weak, significant improvements in credit information have occurred since 2003, notably through the establishment of private credit bureaus with universal coverage.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".