Lehren aus der Krise – Notwendige Änderungen der Finanzmarktregeln und zur Begrenzung systemischer Risiken
Bibliographic record
Abstract
Abstract The latest financial crisis has been caused by a mixture of state and market failure, argues Otmar Issing. To avoid future crises, more transparency is needed - not by gathering more information, but by gathering it systematically and thereby creating “intelligent transparency”. Furthermore, regulation has to be global, he states. The necessary institutions are in place: The International Monetary Fund, the Financial Stability Board and the Bank for International Settlements. Stephany Griffith-Jones and Stefano Pagliari point out, that containing “systemic risk” is one of the most important rationales for regulating financial markets. Our understanding of the sources of systemic risk has repeatedly been challenged by major episodes of financial instability. The crisis that started in the summer of 2007 has been no exception. They discuss how the latest global financial crisis urges analysts and regulators to rethink the origin of systemic risk beyond a narrow focus on the banking sector, beyond the “too big to fail problem”, and beyond a narrow micro-prudential focus. They focus on two regulatory principles: comprehensiveness and countercyclicality. Claudia Buch und Katja Neugebauer review the existing empirical evidence on whether the increase in cross-border activities has allowed banks to diversify risks and to what extent it has increased banks’ exposure to systemic risks.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".