Genetically modified crops, regulatory delays, and international trade
Bibliographic record
Abstract
Abstract Genetically modified ( GM ) crops have been produced in the initial adopting countries for 20 years. Over this period of time, hundreds of articles and reports have been published by academic journals, government regulatory agencies, and national science organizations on the safety aspects of biotechnology and GM crops. In addition to this, there is a growing body of quantified peer reviewed literature on the economic and environmental benefits following the adoption of GM crops in both developed and developing countries. Some estimates place the economic benefits in the billions of dollars a year range. In spite of the documentation of these economic and environmental benefits, GM crops face a challenging future. Environmental nongovernmental organizations ( eNGO s) are relentless in their campaigns of misinformation about the dangers and hazards of GM crops. While eNGO s are unable to quantify their claims and accusations, their political and policy influences continue, particularly in Europe and numerous developing nations. The result of this is regulatory delays for the approval of new GM crops and frequent international commodity trade failures, where shipments have been rejected due to the low‐level presence of a GM crop. Taken in combination, the regulatory and trade challenges facing GM crops are having a detrimental impact on improving food security. This article quantifies the benefits of GM crops, highlights the regulatory costs of delayed approval, and provides insights into the spillover effects from GM crop trade.
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.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".