Bibliographic record
Abstract
This dissertation examines the politics of moral hazard through the development of international standards for managing cross-border bank failures following the 2008 Global Financial Crisis. It addresses two questions. First, why did the post-2008 regime for resolving cross-border banks take on the form that it did, and how has this regime evolved over time? More broadly, how does technocratic governance develop, and what are its implications for global economic integration? In answering these questions, the dissertation develops a theory for understanding the construction of hidden regimes in global governance through a process of bricolage and translation. When delegated responsibility for fundamentally political problems, transnational regulators design hidden regimes by borrowing policy tools from adjacent domains and translating political questions of distribution and sovereignty into technical puzzles about rules and procedures. They do so through two channels: developing arcane regulations which alter the costs and benefits of cooperation, and establishing social practices which foster trust via routine interaction. Such regimes can serve to embed fiscal burden sharing among states in the absence of binding cooperative agreement. However, when economies become too integrated these contingent arrangements can break down. Under these conditions, the externalities of market failure necessitate deeper commitments, otherwise the regime may grow incoherent or ineffective. These insights are developed through three case studies: failed efforts to develop standards for resolving global banks before 2008; a successful effort to develop such standards after 2008 at the global level through the Financial Stability Board; and the uneven transposition of those global standards within the European Banking Union. These case studies are based on archival research and semi-structured interviews with current and former regulators, as well as private sector officials. The results suggest that surface-level failures to end the “too-big-to-fail” problem have masked a significant deepening of international cooperation behind the scenes, which raises questions about accountability and legitimacy in the global governance of moral hazard. While the empirical analysis is limited to the domain of international finance, the results have implications for other spheres of global governance given the increasing tendency for policy to be delegated to informal technocratic networks.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".