The Governance of Markets, Sustainability and Supply. Toward a European Energy Policy
Bibliographic record
Abstract
European energy policy dates back to the founding days of integration, yet the emergence of supranational governance is a recent development. The article examines the extent to which European policymakers have succeeded in building up governance capacity, and what the facilitating and impeding factors were that have shaped the governance mix. The conceptual framework differentiates between orders of governance in the multilevel context, and between policy modes involving hierarchical and non-hierarchical settings and varying actor constellations. The article finds that governance capacity has emerged where second order governance (institutional and procedural rules) is concerned, while first order governance (the concrete policy process) remains the remit of national and private actors. This becomes even more obvious once the interaction between policy modes is taken into account: governance networks enhance governance capacity in the area of competition policy and agency governance; self-regulation by industry constitutes a fall-back option in case of insufficient governance capacity on cross-border issues; soft governance helps to bridge multiple policy areas and levels of governance. The article concludes that second order governance may prove effective where it combines with hierarchy but that it may fail to overcome both trade-offs between contradicting goals and resistance at lower levels.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".