Pastures of Peonage: Tracing the Feedback Loop of Food Through I.P., G.M.O.s, Trade, Immigration, and U.S. Agro-Maquilas
Bibliographic record
Abstract
In this, the final article authored by the late Keith Aoki, we look at interactions among global agribusiness, economic globalization, and labor migration in North America, with specific focus on the United States and Mexico. We highlight the following phenomena: (1) the development of genetically engineered (GE) food crops as genetically-modified organisms (GMOs) and global intellectual property (IP) protection for these crops and other plant genetic resources (PGR); (2) the increasing horizontal and vertical concentration of the agricultural seed-and-chemical, food processing, and food sale industries; and (3) the lack of fit between U.S. immigration law and policy, international trade regimes (such as NAFTA), and the realities of labor migration as related to U.S. agromaquilas in the food picking, processing, and packing industries.We also work to identify and to outline how these seemingly disparate and disconnected phenomena work together in a feedback loop of food production-and-consumption related activities. Intellectual property rights in the realm of global agribusiness and international trade agreements support the oligopolies and oligopsonies in the global food supply chain, which in turn drive the preeminent immigration patterns and demographic changes of North America. This feedback loop of global agribusiness, IP law, international treaties and trade agreements, and immigration law and policy shifts the focus of food supply and the means of its production (including labor and the utilization of farmland) out of or away from Mexico and into or toward the United States.Finally, we consider possibilities for progressive intervention and interruption, in order to reimagine the feedback loop. It is intended that this imagination serve to “push back” against the redundant cycle this article describes and its troubling impacts on the genetic diversity of food crops, the global food supply, small and independent farmers outside the United States, U.S. agromaquila labor migrants, and global labor rights and human rights.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".