Unveiling the Impact: Mexico's Decree on Genetically Modified Corn and its Ramifications on Food Security
Bibliographic record
Abstract
This research delves into the repercussions of Mexico's presidential decree in late 2020, which imposed a ban on the human consumption of genetically modified (GM) corn by January 2024. In a trade dispute under the United States-Mexico-Canada Agreement (USMCA), the decree has sparked tensions between Mexico and the United States, particularly concerning the disruption of GM corn exports. Beyond trade dynamics, the study aims to address the broader impact on Mexico's food security landscape, exploring the intricate connections between the ban and various factors, including agricultural practices, economic implications, and shifts in the corn market dynamics. This research seeks to bridge the communication gap between policymakers and agricultural stakeholders in Mexico and the United States, focusing on the input of smaller producers in Oaxaca, Mexico. By addressing existing knowledge gaps, the research aims to provide a nuanced analysis of economic, agricultural, and cultural factors shaping the trajectory of food security in Mexico. The intention is to inform policymakers, agricultural stakeholders, and the broader community about the potential challenges and opportunities arising from Mexico's stance on GM corn and its implications for the nation's overall food security. The synthesis of findings suggests that the ban on genetically modified seeds has the potential to bolster food sovereignty in Mexico, safeguarding the food security of rural populations reliant on sustenance and traditional farming practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".