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Record W2947218642 · doi:10.30950/jcer.v15i2.1011

Researching Technocracy: Actor-Centred Methodologies and Empirical Strategies for Studying EU Economic Governance in Hard Times

2019· article· en· W2947218642 on OpenAlexfundno aff
Daniel F. Schulz

Bibliographic record

VenueJournal of Contemporary European Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersUniversity of VictoriaUniversité du Luxembourg
KeywordsTechnocracyProcess tracingCorporate governancePolitical scienceEuropean unionElitePoliticsPublic administrationPositive economicsRegional scienceSociologyEconomicsEconomic policyLawManagement

Abstract

fetched live from OpenAlex

The euro’s problem-ridden second decade has made crisis management and economic reform across the European Union (EU) the priority of high politics. Despite the prominence of high-level intergovernmental summits, however, many studies identify EU policymaking elites as influential or even causal factors determining the EU’s crisis response. This commentary therefore reviews the recent literature on EU economic governance which emphasises the role of supranational actors. The focus is on the methodologies and empirical strategies that scholars employ to determine the independent effect of policymaking elites on outcomes. This commentary identifies a renewed interest in actor-centred methodologies as well as a continuing emphasis on process-tracing approaches, primarily based on elite interviewing and document analysis. Finally, it discusses the potential of novel approaches relying on other sources of data such as policymakers’ biographies, their speeches, or publication and citation patterns.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.431
GPT teacher head0.491
Teacher spread0.061 · 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 teacher head, 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

Citations3
Published2019
Admission routes1
Has abstractyes

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