Changes in monetary policy after the crisis - towards preventing banking sector instability
Bibliographic record
Abstract
The instability of the banking sector has become the subject of wider scientific research during the global financial crisis. The financial crisis of the first decade of the twenty-first century began in the U.S. subprime mortgage market and quickly spread to the whole banking sector in the United States as well as in many countries of the global economy. Among five major American investment banks - Lehman Brothers went bankrupt, Bear Stearns and Merrill Lynch were taken over by other banks, and Goldman Sachs and Morgan Stanley were transformed into commercial banks, which were covered by the supervision and regulations of the central bank - the Federal Reserve System. The consequences of the global financial crisis also affected British banks, including The Royal Bank of Scotland, Lloyds Bank, Halifax, Abbey Bank, Barclays Bank and NBC Bank. In Iceland, during the global financial crisis which affected the Icelandic banking sector, three largest banks: Glitnir Bank, Landsbanki and Kauphting were nationalized, which means that the control was taken over by their government. It has caused, that reflections and scientific research on financial stability were replaced by the study of instability in particular in relation to the banking sector. The main aim of the study is to identify the general framework of the response system of central banks on the phenomenon of banking sector instability, in the context of preventing it in a long term. Current - the traditional system proved to be ineffective, because it did not prevent the spread of the factors that led to the destabilization of the banking market
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".