China's crisis management and market exit mechanism for banks—What is the way forward?
Bibliographic record
Abstract
Abstract As China's crisis management and market exit mechanism for banks (CMME mechanism) is still at an early stage of development, the legislation governing the mechanism is far from sufficient or satisfactory. With the aim of exploring how the holistic framework of China's CMME mechanism can be improved, this article systemically examines the mechanism from the perspectives of the major procedural components and the specially designed funding sources. Based on the analysis, three major types of weaknesses in the current mechanism are pointed out: ‘missing elements’, ‘unfit elements’ and ‘uncoordinated elements’. To be specific, some essential elements are missing from the current mechanism; some elements in the mechanism are unfit when applied to banks; and some elements are uncoordinated with each other within the mechanism. It is necessary that an overhaul of the mechanism be carried out to address these weaknesses. In addition, given that governments' involvement was demonstrated to be helpful in resolving bank crises in past cases, it would be better to institutionalise this experience in the CMME mechanism. Only with a well‐crafted CMME mechanism can bank crises be resolved in an orderly, effective and efficient manner.
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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 | 0.004 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".