Governing Agricultural Biotechnologies in the United States, the United Kingdom, and Germany: A Trans-decadal Study of Regulatory Cultures
Bibliographic record
Abstract
Comparative studies of agricultural biotechnology regulation have highlighted differences in the roles that science and politics play in decision-making. Drawing on documentary and interview evidence in the United States, the United Kingdom, and Germany, we consider how the “regulatory cultures” that guided national responses to earlier generations of agricultural biotechnology have developed, alongside the emergence of genome editing in food crops. We find that aspects of the “product-based” regulatory approach have largely been maintained in US biosafety frameworks and that the British and German approaches have at different stages combined “process-based” and “programmatic” elements that address the scientific and sociopolitical novelty of genome editing to varying degrees. We seek to explain these patterns of stability and change by exploring how changing opportunity structures in each jurisdiction have enabled or constrained public reasoning around emerging agricultural biotechnologies. By showing how opportunity structures and regulatory cultures interact over the long-term, we provide insights that help us to interpret current and evolving dynamics in the governance of genome editing and the longer-term development of agricultural biotechnology.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".