The Inclusion of Nonsafety Criteria within the Regulatory Framework of Agricultural Biotechnology: Exploring Factors that Are Likely to Influence Policy Transfer
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
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 of competition and coercion do 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 of mimicry and learning can foster the transfer of such a broadened scope that allows a better assessment of GM foods' social acceptability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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