Scientific Credibility, Disagreement, and Error Costs in 17 Biotechnology Policy Subsystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the original objectives of the advocacy coalition framework (ACF) was to shed light on the role of science in policymaking. The ACF depicts subsystem scientists as political actors just like any other. Unfortunately, science has never become a major theme of research within the framework and, as a consequence, its role in policymaking remains under‐theorized, leaving ample room for interpretation. This article seeks to explore the validity of three propositions about the role of science in policy. The first two are derived from the ACF: (i) the capacity of scientists to provide credible advice is affected by the harshness of the political debates dividing the policy subsystem; and (ii) agreement among scientists is just as common as among other groupings of policy actors. The third is derived from an “error costs” argument: (iii) Disagreements among scientists are even more pronounced than disagreements among other policy actors. Using the results of a survey of policy actors in 17 biotechnology subsystems, this article finds support for the first and third propositions. Indeed, scientists' participation in political divisions might even be underestimated by the ACF. The article concludes with attempts to clarify the role of scientists within the ACF, including discussions of ambiguity regarding the role of professional forums and of scientists in between‐coalition learning within policy subsystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it