Biotechnology regulation: is policy transfer an appropriate answer?
Bibliographic record
Abstract
In the world of biotechnology regulation, one often encounters the suggestion that the legislation of other countries should be consulted. Known as "policy transfer" in the field of public policy analysis, the purpose of such a recommendation is for policymakers to use the experiences of other States as a basis for developing appropriate regulatory frameworks in a timely manner. This paper examines whether policy transfer is relevant as an instrument for biotechnology regulation, and if it is, to what extent. Our analysis uses the example of Assisted Reproductive Technologies (ART), and unfolds according to the following argumentative steps. We will begin by discussing policy transfer as a recognized feature of policymaking in the literature pertaining to public policy analysis. We will then introduce a distinction between the technical dimension of policymaking and its political component. We will refer to "morality policy" as an illustration of policymaking directed toward its political component. We will show that, in the case of morality policy, States have moved away from a policy transfer approach. We will then establish that ART qualifies as morality policy, suggesting that policy transfer is most likely not the optimal policymaking tool for dealing with biotechnology regulation. Moving beyond the issue of ART in order to expand our reasoning to biotechnology regulation as a whole, we will conclude that, although the experiences of other States may be useful, policy transfer does not suffice in terms of informing policymaking in the case of biotechnology advances.
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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 | 0.055 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.019 | 0.047 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.031 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".