Campos agrícolas, campos de poder: el Estado mexicano, los granjeros canadienses y los trabajadores temporales mexicanos
Bibliographic record
Abstract
El artículo aborda el análisis del Programa de Trabajadores Agrícolas Temporales entreMéxico y Canadá a través de los conceptos “campos sociales” y “campos de poder” paraentender la aparente contradicción entre la satisfacción abiertamente expresada por losparticipantes en el programa y el alto grado de control, trabajo intensivo y bajos salariosque muchos trabajadores padecen en Canadá. Solamente cuando observamos la maneracomo los migrantes “tejen” diferentes campos de poder –el campo de relaciones sociales enCanadá y el campo local formado por las relaciones en la comunidad en que viven–podemos entender que al ir a trabajar a Canadá, lo que implica un rompimiento de loslazos con sus familias y comunidades, los trabajadores migrantes pueden cumplir con lasexpectativas morales y culturales de sus localidades.ABSTRACTThis essay analyses the Temporary FarmWorkers Program between Mexico and Canadausing the terms “social fields” and “power spaces” to understand the apparent contradictionbetween the satisfaction expressed by the participants in the program and high levels ofcontrol, intensive work and low wages that the workers suffer in Canada. Only when weobserve the ways the migrants “weave” the different power spaces—in Canada’s socialrelations space and the space shaped by the relations in the community they live—we canunderstand that to be working in Canada, that means to break their family and communitybonds, the workers can comply with the local moral and cultural expectations
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".