Drawing Lines in the Sand? Paths Forward for Triggering Regulation of Gene-Edited Crops
Bibliographic record
Abstract
Abstract Researchers are making use of new gene-editing techniques in medicine, bioenergy, industrial biotechnology, and beyond, and the field of crop breeding is no exception. These techniques, which differ from genetic modification techniques, spell difficult questions for regulatory oversight: will current rules-of-play apply, or do new techniques necessitate fundamental shifts in regulations? Thus far, little explicit attention has focused on the fundamental yet elusive questions of which technical specifics currently trigger regulation of gene-edited crops, and where different jurisdictions ‘draw’ this line. Here, we trace these regulatory lines across key jurisdictions. We argue that extant regulatory definitions are crumbling in the face of emerging technologies and assert that this breakdown poses a threat to responsible governance. Drawing upon insights from responsible research and innovation, we propose a shift away from technically based regulatory approaches and toward more risk-targeted oversight based on broader societal and ecological implications.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".