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Record W2587028237 · doi:10.1080/07036337.2016.1277715

Political leadership of the European Central Bank

2017· article· en· W2587028237 on OpenAlexaff
Amy Verdun

Bibliographic record

VenueJournal of European Integration · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversity of Victoria
FundersEuropean Commission
KeywordsSovereign debtEuropean debt crisisFinancial crisisSovereigntyCorporate governancePoliticsPolitical scienceEconomicsDebtFinancial systemTransformative learningMonetary policyPolitical economyEconomic policyFinanceEuropean unionEuropean integrationSociologyLawKeynesian economics

Abstract

fetched live from OpenAlex

What role did the European Central Bank (ECB) play in EU governance, regarding the financial, the economic and sovereign debt crises? How should we understand ECB leadership from a theoretical perspective? Based on speeches, literature review and interviews, this contribution concludes that by using policies such as the Securities Market Programme and by promising to do whatever it takes (e.g. Outright Monetary Transactions the ECB supported the euro-area faced with an unprecedented crisis. Its two presidents during the crisis periods were leaders in that they managed to get the ECB followers (its Governing Council; EU member states) willing to take part in a common enterprise solving the sovereign debt crisis with the bank using exceptional monetary policy tools. This contribution argues that both Presidents Jean-Claude Trichet and Mario Draghi exercised transformative leadership and were willing to take action when no other leaders were willing or able to lead.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0100.002
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.105
GPT teacher head0.330
Teacher spread0.225 · 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 designNot applicable
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

Citations65
Published2017
Admission routes1
Has abstractyes

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