Agrifood field analysis and sociocultural brokerage. Mexico and the United States: 1950–2016
Bibliographic record
Abstract
Abstract In this work, we propose the heuristic and explicative possibilities of agrifood fields analysis (AFFA) for the historical study of capitalism in agriculture and food. This approach considers the agroclimatic conditions of the territories and the sociocultural plurality of the actors who inhabit them. This phenomenological and eco‐territorial approach allows to study, on many scales, the networks of social relations in which actors take part who are competing for the benefits that are created by human labor applied to producing certain foods and making them accessible to consumers. To show the value of the AFFA, we present a historical study of the agrifood field that was developed from the production of fresh fruits and vegetables in the Rio Grande Valley in South Texas and several regions of Mexico for the market in the United States and Canada. The concept of sociocultural brokerage allowed us to take into account the social and cultural plurality in the AFFA and the role played by the creativity and skills of the actors in managing the resources of knowledge, society, technology, and capital at their disposal. This way we were able to explain the dynamics of power and the changes in agrifood governance in the field.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".