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Record W2573568149 · doi:10.1017/gov.2016.49

When Some Are More Equal than Others: National Parliaments and Intergovernmental Bailout Negotiations in the Eurozone

2017· article· en· W2573568149 on OpenAlexaff
Manuela Moschella

Bibliographic record

VenueGovernment and Opposition · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsBailoutCreditorNegotiationConditionalityLegitimacyArgument (complex analysis)EconomicsPolitical scienceEconomic and monetary unionPoliticsPolitical economyEuropean unionInternational economicsFinanceFinancial crisisLawDebtMacroeconomics

Abstract

fetched live from OpenAlex

This article argues that the integration of financial assistance capacity in the eurozone, which was meant to remedy institutional shortcomings and mitigate the distributional implications of financial support in the European Monetary Union (EMU), has instead contributed to a deepening of the existing political cleavages and the creation of new ones. This dysfunctional effect reflects the empowerment ofsomenational parliaments in decisions on financial assistance. These arguments are tested against the empirical examination of the negotiations of the three adjustment programmes for Greece. Specifically, the article shows that negotiations moved towards the radicalization of creditors’ positions and increased divisions between creditors in conjunction with the development of financial assistance capacity. While advancing its theses, the article strikes a note of caution regarding the argument that the empowerment of national parliaments in EU policymaking is one of the most powerful antidotes to its legitimacy deficit and thus a safeguard for the integration project.

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)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0100.018
Scholarly communication0.0140.007
Open science0.0010.010
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.310
Teacher spread0.272 · 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 designObservational
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

Citations98
Published2017
Admission routes1
Has abstractyes

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