Socio-Economic Considerations and Potential Implications for Gene-Edited Crops
Bibliographic record
Abstract
Regulatory clarity and efficiency are increasingly important for the successful commercialization of technologies resulting from public and private R&D investments. This article examines recent developments in the movement away from mostly science-based risk assessment regulatory and variety approval systems focusing on human and animal health and environmental safety to hybrid systems that include assessment of socio-economic considerations allowed for under the auspices of the Cartagena Protocol on Biosafety. We propose that socio-economic consideration assessments can be grouped into three methodological categories: empirically based, legally grounded and consensus approaches. Our exploration of developments in the three categories reveals gaps in data, consistency and methodology rigor, that must be addressed for efficient and reliable socio-economic consideration assessment. We assess the potential impacts of these gaps on the regulation of gene-edited crop varieties, concluding that if gene-edited crops are regulated as genetically modified crops, they will endure the same fate, that is, lengthy regulatory approval processes and failure to be commercialized. The end result being that the regulatory burdens in potential adopting and food insecure countries will prevent important new crop varieties from reaching farmers and producers for their use. https://doi.org/10.21423/jrs-v09i2ludlow
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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 | 0.043 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".