The G20's Reform of Bank Regulation and the Changing Structure of the Global Financial System
Bibliographic record
Abstract
Abstract The G20's financial regulatory reform programme, underway since 2008, was designed as an integrated set of strengthened regulatory measures to make the global financial system more robust and less crisis‐prone. However, in practice the G20's stricter regulation of internationally active banks has not been matched by firmer oversight of other financial system participants that also take on large risks. The present article addresses the implications of this situation. Section 1 outlines the role of the banking sector within the broader market‐based financial system. Section 2 considers the dynamics of a crisis in such a system. The measures that the G20 has agreed thus far to strengthen the G‐ SIB s, and regulators’ intentions with regard to strengthening other sectors of the financial system are summarized in Section 3 . Section 4 describes how stricter bank regulation may affect the resilience of market‐based financial systems in conditions of stress. Section 5 discusses how the focus on strengthening bank regulation, combined with slow progress in extending regulation to other sectors, has accelerated structural change in the financial system and offers some conjectures on how this may alter financial risks, the way the financial system may respond to stress, and the implications for macroprudential regulation.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".