Racializing Trade in Corn: México Fights <i>Maíz</i> Imports and GMOs
Bibliographic record
Abstract
ABSTRACT International economic law (IEL) is racialized in México. This is evident with the North American Free Trade Agreement, US Mexico Canada Agreement (USMCA), and ongoing litigation changing how Mexicans grow, buy, and consume corn. Racialization develops from law privileging foreign access, at the cost of domestic abilities to counter economic entry. This has domestic and international consequences. Mestizo, campesino, and Indigenous sectors disproportionately bear the domestic effects of corn imports and GMO (genetically modified organism) corn. Imports and GMOs displace rural labor. Meanwhile, GMOs irreparably harm biodiversity. The USMCA protects ‘trade in’ GMOs while simultaneously discounting sovereign determinations by Mexican regulators. This echoes international law’s history of racist reasoning to exclude Global South states. This article proposes racial capitalism methods to pinpoint how and where IEL racializes cross-border trade. They show how neoliberal laws resulted in price spikes, labor migration, and unhealthy diets. Current GMO controversies open the door to two IEL paths: resistance or market access. As resistance, Mexican court rulings based on Precautionary Principle norms prohibit GMO corn seeds. With market access, the USMCA harmonizes biotechnology regulations, effectively trumping determinations by Mexican authorities. In sum, as neoliberal and current developments show, IEL racializes trade with disproportionate consequences felt domestically and by displacing Mexican sovereignty internationally.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".