Enforcement condiviso in contesti multilivello. Italia e Unione Europea nella Politica comune della pesca. (Shared Enforcement of the European Regulation Facing Political Accountability: Italy and the Common Fisheries Policy. With English summary.)
Bibliographic record
Abstract
Studies on the effectiveness of regulation are increasingly focusing their attention on all phases of decisionmaking. With regard to European regulation, most gaps in effectiveness occur during implementation, so far the competence of Member States. To strengthen its own intervention and to provide European regulation with more effectiveness, EU institutions are increasingly acquiring direct enforcement powers. Regarding the effects of this growing shared enforcement, a preliminary question is whether such mechanisms raise any problems with regard to democratic accountability, which is, as a matter of common knowledge, a long-standing concern for European institutions and governance. The CFP is a suitable example of this trend towards more direct action by the EU in enforcing regulation, where, though, mechanisms of accountability have not undergone changes accordingly. The paper takes mechanisms of shared enforcement within the CFP into account, to assess whether they rely on adequate mechanisms of political accountability, therefore displaying gaps that cast some shadow on the legitimacy itself of the way shared enforcement policies are set up, and on the system of European citizens' protection.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".