Regulating biomedicine in Europe and North America: A qualitative comparative analysis
Bibliographic record
Abstract
Abstract This article explains the variation in policy design processes and the resulting policy‐outputs of ‘biopolicies’ implemented within the domain of Assisted Reproductive Technology (ART) for eleven European and North‐American countries. By applying the method of Qualitative Comparative Analysis, the comparison describes and defines the ‘multiple conjunctural causation’ to explain the divergences or similarities of ART policies in Europe and North America. The policy preferences of the actors involved in the relevant ART policy network and the institutional rules characterizing the respective polity need to be considered together in order to explain why different countries adopted similar or different ART policies. In particular, the analysis stresses the influence of party politics, the self‐regulation of ART by the physicians, the mobilization of interest groups, the number of institutional arenas involved in the designing process and the nature of decision‐making rules (power‐sharing versus majority) on the designing processes and the resulting policies. Thus, different policy designs are linked to different designing processes, encompassing four ideal‐typical decision‐making modes: ‘designing by non‐decisions’, ‘designing by elites’, ‘designing by accommodation’ and ‘designing by mobilization and consultation’. These results shed new light on the challenges for developing a policy design theory that could provide a robust framework for describing and explaining policy formulation.
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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".