EU soft law in the member states:Theoretical findings and empirical evidence
Bibliographic record
Abstract
This volume analyses, for the first time in European studies, the impact that non-legally binding material (otherwise known as soft law) has on national courts and administration. The study is founded on empirical work undertaken by the European Network of Soft Law Research (SoLaR), across ten EU Member States, in competition policy, financial regulation, environmental protection and social policy. The book demonstrates that soft law is taken into consideration at the national level and it clarifies the extent to which soft law can have legal and practical effects for individuals and national authorities. The national case studies highlight the points of convergence or divergence in the way in which judges and administrators approach soft law, while reflecting on the reasons for and consequences of various national practices. A series of horizontal studies connect this research to the rich literature on new modes of governance, by revisiting traditional theories on soft law, and by reflecting on the potential of such instruments to undermine or to foster rule of law values.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".