Influence of GMO Regulations on GM Crop Adoption in Developing Nations that Export
Bibliographic record
Abstract
This study examines the pivotal role of GMO regulations of the exporting developing countries on their farmers’ adoption of GM crops. We focus on one or two major crops, including cotton, maize and soybean, in each of the five exporting developing countries: Argentina, Brazil, China, India, and Mexico. Various aspects of the import regulations of GMO products are captured by four indices. The relative impact of GMO regulations on exports is assessed to better understand farmers’ domestic production decisions in exporting developing countries. The results show that not all the elements of GM regulations or measures significantly affect the percentage of farmers adopting GM crops. However, the farmers play a significant role in this adoption. Labeling requirements stand out in that they were positively associated with the entire sample and the Latin countries' sample. The risk assessment measure had a slightly negative association with the GM adoption rate.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".