What finance wants: explaining change in private regulatory preferences toward sovereign debt restructuring
Bibliographic record
Abstract
It is widely assumed that when private financial actors seek to influence the regulation of global finance, their preference is for fewer or weaker rules. But is this preference tied to fixed material interests, or is it more malleable? Can it change over time? If so, why might it? I address these questions by examining a recent shift in private creditor preferences toward the regulation of sovereign debt restructuring. I argue that changed material circumstances created demand for regulatory change among creditors but did not determine the nature of their preferred solution. Instead, it was shifts in collectively-held ideas—the specific content of which was informed by important historical and contextual factors—that led private creditors to embrace stronger debt restructuring rules, despite being historically opposed to such rules. In making this argument, the article contributes to a fuller understanding of private market actors in global financial politics, challenging the assumption that these actors necessarily prefer weaker rules, and highlighting the more contingent nature of their regulatory preferences. It also contributes to wider debates about preference formation and change, highlighting important complementarities between distinct theoretical traditions, which together provide a much richer explanation of the case at hand.
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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 | 0.009 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".