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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".