Researching Technocracy: Actor-Centred Methodologies and Empirical Strategies for Studying EU Economic Governance in Hard Times
Bibliographic record
Abstract
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.
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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 | 0.084 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".