Governing Sovereign Debt Restructuring Through Regulatory Standards
Bibliographic record
Abstract
Abstract In recent years, a number of costly and destabilizing sovereign debt crises – from Argentina and Greece to Ukraine – have served as a forceful reminder that the international community lacks an agreed-upon framework for resolving debt crises and, when necessary, restructuring sovereign debt in a timely, orderly, and equitable manner. To help address this apparent governance gap, the paper argues that there is an important but underutilized role for the Financial Stability Board (FSB) in governing sovereign debt restructuring. More specifically, in a governance domain that is relatively fragmented between uncoordinated, even sometimes competing, rules and rule-makers, the FSB could serve as the focal institution responsible for overseeing the coordination and further development of soft law regulatory standards for sovereign debt restructuring. The reasons for FSB governance in this domain are simple and compelling, relating to both the nature of the debt restructuring regime and its evolution to date, as well as the specific institutional features of the FSB and the core tasks it performs. Although there remains room for treaty-based organizations like the International Monetary Fund (IMF) and United Nations (UN) to develop a hard law approach to sovereign debt restructuring, the FSB, we argue, is best positioned to strengthen and oversee the existing soft law approach, which currently prevails as the modus operandi of the present debt restructuring framework.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".