Similar Regulatory Challenges but Contrasting Modes of Governance? The Puzzle of Governing Human Biotechnology across Western Europe
Bibliographic record
Abstract
Human biotechnology is a fairly recent policy issue that emerged onto the political agenda in the 1980s and 1990s in most Western European countries. As an emerging policy area, human biotechnology was largely unstructured and the governance was mostly left to the medical and scientific communities. In the meantime, with the exception of Ireland, all Western European states have designed regulation, yet governance modes still vary considerably. Various governance modes have been developed. Some of these modes rely on traditional “command and control” governing arrangements, others operate with delegated or partial self-governance. While the government has taken a more pre-eminent role over time in the governance of the field, some striking variation remains. The challenge is to understand when, how and under what conditions modes of governance emerge and evolve over time. In this chapter, we shed light on the impact of the dynamics between stakeholders in building up modes of governance over time. We argue that the variation in the configuration of and the interactions between stakeholders, in particular medical and scientific stakeholders, impacts on the trajectory of governance modes over time. The continuous process of structuring a new policy problem is tightly linked to establishing modes of governance (Capano et al. 2012). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 | 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 | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".