The potential of soft governance in the EU information society: lessons from the EU electronic communications regulatory framework
Bibliographic record
Abstract
The paper has two key findings. First, the EU’s employment of soft governance as a policy tool to stimulate effective transposition and implementation of agreed measures in telecommunications has been utilised alongside the threat (and use of) hard legal sanction. Though something of a complementary tool in this respect, evidence suggests that it has only been partially successfully. This is clearly illustrated, somewhat ironically, by the claims made by the European Commission in its most recent review of the telecommunications regulatory package, of ineffective compliance by Member States, despite many years of soft governance activity. An interesting feature is the prominent ‘naming and shaming’ aspect of soft governance which has become, in effect, a victim of its own circumstance, since the longer it persists, the more evidence of its only partial effectiveness mounts. It is also the case that such activity can sour relations between the European Commission and national regulatory authorities, with knock on implications for any attempts made by the former to increase its hard regulatory powers of enforcement. Relatedly, the second finding of the paper centres on the EU’s more limited drawing on soft governance as a product of decision-taking, highlighted by the example of the EU’s telecommunications regulatory remedies procedure. The precise outcome here has been ‘hard’ legislation in the shape of a directive which stipulates soft governance measures. The paper argues that, in this case, there is evidence to reinforce existing work which suggests that soft governance has been utilised as a tool of piecemeal inter-institutional political compromise in controversial negotiating circumstances.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".