The changing contours of experimental governance in European health care
Bibliographic record
Abstract
For over a decade, beginning in the late 1990s, discussion over softer modes of governance animated academic scholarship in the fields of law, politics, and public policy. This debate was especially pronounced in Europe. Since the late 2000s, however, discussion of this approach has declined precipitously. Is the "soft governance" model dead? Or, more precisely, has the economic crisis killed it? This article argues that, to the contrary, the EU's austerity measures have made softer governance more relevant in two quite distinct ways. Administratively, new mechanisms of health policy coordination are able to provide policy solutions in a much more effective way than could more formal and rigid forms of legal harmonisation. Politically, it establishes a normative perspective which unifies actors across a number of administrative units and challenges the dominant ideological force of the market-based principles upon which the EU's austerity policies are constructed.
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 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 | 0.065 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".