MétaCan
Menu
Back to cohort
Record W2994323170 · doi:10.1163/19426720-02503004

The Politics of Regulatory Design in the Sovereign Debt Restructuring Regime

2019· article· en· W2994323170 on OpenAlexaff
Skylar Brooks

Bibliographic record

VenueGlobal Governance A Review of Multilateralism and International Organizations · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Regulation and Crises
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsRestructuringPoliticsSovereigntyDebtSovereign debtInternational lawDebt restructuringLaw and economicsPublic international lawPolitical sciencePolitical economyEconomicsLawFinance

Abstract

fetched live from OpenAlex

Abstract This article looks at two recent initiatives aimed at improving sovereign debt restructuring processes and asks why one initiative succeeded while the other failed. It argues that the success or failure of a reform initiative in the debt restructuring regime depends primarily on the legal-institutional design of the mechanism it is advancing. Hard law mechanisms face enormous political obstacles that make their realization unlikely. Several of these obstacles have been identified by previous studies, but this article highlights additional barriers. It also shows that, in contrast to hard law arrangements, private law contracts provide politically useful mechanisms for regulating debt restructuring, especially for powerful states with major influence over reform outcomes—namely, the United States. The article also argues that the historical legacy of earlier reform initiatives matters, but mainly through its ability to further enhance or diminish the political prospects of mechanisms whose utility has already been determined by their design features.

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 imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.026
Scholarly communication0.0120.005
Open science0.0010.004
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.015
GPT teacher head0.241
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Governance A Review of Multilateralism and International OrganizationsSame topicGlobal Financial Regulation and CrisesFrench-language works237,207