CLE Working Paper No. 2/2022--Mexican Salsa and Mexican Farm Workers: How International Agricultural Development Marginalizes Farm Labour
Bibliographic record
Abstract
International food systems have become ever-more complex through systems of globalization, industrialization and technologization, and have been significantly influenced by, and entrenched in concepts of international development. One small meal can have countless intersections with international laws, domestic laws, environments and people. A simple salsa recipe, for example, containing merely tomatoes, lime juice, garlic, onions, and cilantro, contains in its history a complex story of power, privilege, poverty and possibility. Where did these ingredients come from? Who grew them? Where are those people from? What rights do they have? Innumerable personal stories are hidden within the seemingly innocuous act of eating salsa.\nMy paper will contribute to discussions on how models of international agricultural development impact human rights, through an anthropological lens that traces salsa ingredients back to their source. Although often praised as beneficial, and even necessary, my paper will argue that the current model of international agricultural development is counter-productive for concepts implicit in the ethos of development, including human rights, health, and resiliency. By tracing ingredients back to their source: limes from Mexico; tomatoes from California; Onions from Ontario; and Garlic and Cilantro from British Columbia, my paper will discuss how models of international development have worked to dispossess communities from their land, and continue to uphold structures of poverty for those who grow our food. My paper will also discuss possible alternatives to the current model of agricultural development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".