Governing Finance: Global Imperatives and the Challenge of Reconciling Community Representation with Expertise
Bibliographic record
Abstract
Abstract Although the regulation of financial institutions and global markets has been subject to extensive research and policy practice, regulation often comes second to governance: regulation cleans up failures of governance in the management and performance of private financial institutions and markets. There are two theories of the nature and practice of governance; one emphasizes its functional performance, whereas the other emphasizes its political foundations. In this article, I suggest that best practice seeks to reconcile functionalism with community representation and that representation is a virtue in its own right and need not be seen as antithetical to functional efficiency. To sustain these arguments, I note the distinctive characteristics of financial decision making under risk and uncertainty, using simple examples to underscore the benefits of good governance. I then present criteria for well‐governed financial institutions, specifically public and private pension funds, with implications for best practice as illustrated by four case studies of funds from Canada, Europe, and the United States. The final section considers the lessons of these case studies for the design of sovereign wealth funds and raises questions as to whether there are limits to reconciliation, given the acceleration of global financial integration.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".