Law, Food, and Culture: Mexican Corn's National Identity Cooked in 'Tortilla Discourses' Post-TLC/NAFTA
Bibliographic record
Abstract
This essay offers a brief inquiry concerning food and national identity as expressed in the law. It examines how in 2008 the Tratado de Libre Comercio de America de Norte/North American Free Trade Agreement (TLC/NAFTA) eliminates Mexican tariffs for corn imports from the U.S. or Canada. Corn is examined as a product steeped in centuries of cultural significance for Mexico. This essay prepares a three-course argument. Section I incorporates insights from the food studies discipline to argue that beyond serving for nourishment food possesses enormous cultural and commercial value. This creates a ripe and abundant subject for legal analysis, focusing on how the law frames these tastes. Section II analytically serves up the cultural importance of food in Mexico's political economy. It shows how food is stewed within a discourse of national identity on a global table. This identity is imagined as a community with competing menu options of nationalistic and domestic and foreign and neo-liberal. Current tariff elimination resembles a historic and cultural tortilla discourse, which poses corn and its use by popular sectors against modern interests. Section III describes a re-imagination of food and national identity within the confines of Mexican law and the recent tortilla price crisis.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".