When doctors shape policy: The impact of self‐regulation on governing human biotechnology
Bibliographic record
Abstract
Abstract This paper investigates the development and adoption of governance modes in the field of human biotechnology. As the field of human biotechnology is relatively new, voluntary professional self‐regulation constituted the initial governing mode. In the meantime, with the exception of Ireland, all Western European countries have moved toward greater state intervention. Nevertheless, they have done so in contrasting ways and the resulting governance modes for assisted reproductive technology and embryonic stem‐cell research vary greatly. Instead of imposing their steering capacity in a “top‐down” fashion, governments have taken pre‐existing self‐regulatory arrangements in the field into account and built up governance mechanisms in conjunction with private actors and pre‐existing modes of private governance. Our analysis demonstrates that the form and content of the initial self‐regulation explain why the self‐steering capacity of the medical profession was largely or at least partially preserved through hybrid governance systems in Britain and Germany, while in France the self‐regulation was entirely replaced by governmental intervention.
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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 | 0.048 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".