Governing gene-edited crops: risks, regulations, and responsibilities as perceived by agricultural genomics experts in Canada
Bibliographic record
Abstract
This paper explores the role and responsibilities of agricultural genomics experts in governing gene editing (GE) for food and agriculture, engaging with the frameworks of technological determinism and Responsible Research and Innovation (RRI). We interview agricultural genomics experts in Canada to study expert views on risks, benefits, and regulatory challenges of GE crops and the extent to which agricultural genomics experts exercise the RRI principles of anticipation, reflexivity, deliberative inclusion, and responsiveness. Agricultural genomics experts wield power in food systems both in shaping the applications of technology and as advisers influencing policy and governance. Their resistance to RRI principles, especially deliberative inclusion and responsiveness, and exercises of discursive closure are challenges for responsible governance of GE crops. The study offers empirical and theoretical contributions, working across Science and Technology Studies and food systems research.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".