Experimentalist governance in the<scp>E</scp>uropean<scp>U</scp>nion: A commentary
Bibliographic record
Abstract
Abstract Sabel andZeitlin'sExperimentalistGovernanceoffers an insight intoEuropean governance in those cases where theEUinstitutions do not have clear competence and where member states are not prepared to accept a unified policy on a problem at hand.ExperimentalistGovernanceidentifies four steps of action: agree on common goals, have lower levels propose ways to meet goals, then report on their meeting of goals, and, finally, periodically reevaluate the review procedures. By looking at the developments inEUpolicymaking through the lens of experimentalist governance (EG), one obtains an appreciation of how goals might be achieved that would otherwise not likely have been achieved through the community method.Sabel andZeitlin highlight howEGcan be effective in obtaining results, integrating peers, and incorporating deliberation, and offer a different way to deal with accountability and legitimacy. This article closes by taking the next step, namely, asking what challengesEGposes to democratic processes.
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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 | 0.054 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.049 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".