The Inclusion of Nonsafety Criteria within the Regulatory Framework of Agricultural Biotechnology: Exploring Factors that Are Likely to Influence Policy Transfer
Bibliographic record
Abstract
Abstract Policy makers of various countries are exposed to critiques that call for the consideration of issues that transcend human, animal, and environmental safety concerns when assessing agricultural biotechnology products. While some jurisdictions have decided to broaden the scope of their approval process for genetically modified (GM) foods, this paper analyzes legal, political, and economic factors that can influence the transfer of these initiatives. Drawing on mechanisms presented in the policy transfer literature, this article examines their mixed effects pertaining to the regulation of biotechnology. Although the mechanisms ofcompetitionandcoerciondo not preclude such a possibility, one must admit that they do not create any incentives for policy makers to include nonsafety criteria within biotechnology regulations. By contrast, to varying degrees, the mechanisms ofmimicryandlearningcan foster the transfer of such a broadened scope that allows a better assessment ofGMfoods' social acceptability.
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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 | 0.068 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".