www.newschool.edu/cepa THE POLITICS OF GLOBAL FINANCIAL REREGULATION: LESSONS FROM THE FIGHT AGAINST MONEY LAUNDERING
Bibliographic record
Abstract
Canada. I am also grateful for comments from John Eatwell, Lance Taylor, and other participants in workshops associated with the preparation of this book. 1 Reform of the global financial system has emerged as one of the central issues on public policy agendas around the world. In normal times, the public rarely shows much interest in global financial issues. Seemingly arcane and technically complex, the subject is left to specially trained economists, practitioners in the markets, and financial journalists to debate. But these are hardly normal times. Developments during the last few years have highlighted to all some of the costs associated with the dramatic globalization of financial markets: diminished national policy autonomy, volatile exchange rates and a new vulnerability to systemic financial crises. Indeed, it was the desire to avoid these costs that led the architects of the Bretton Woods system over fifty years ago to endorse the use of capital controls and a much more regulated international financial system than we now live in. Particularly prominent in the new debate on global financial reform is the widespread interest in the reregulation of global financial markets. Gone is the rhetoric of
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.343 | 0.068 |
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".