Examining Indigenous perspectives on the health implications of large‐scale agriculture in Jalisco, Mexico
Bibliographic record
Abstract
In Mexico, as in many other parts of the world, industrial agriculture is dramatically changing rural landscapes and altering relationships with the land. This paper draws on community‐based research from a collaborative international research project that examined the perceived health implications of the agricultural industry for Indigenous peoples in the state of Jalisco, Mexico. Thirty interviews were conducted in a Nahuas community experiencing expanding agribusiness industries. The results of this study show that the implications of export‐oriented agricultural industry for this Nahuas community are complex and, at times, contradictory: employment in the agricultural industry provides community members with much‐needed sources of income, but it is precarious work. At the same time, community members are concerned about the long‐term health and environmental implications, such as increased exposure to chemicals, depletion of the soil and water, and loss of traditional food and lifeways. These results suggest that to better understand the costs and benefits of large‐scale agriculture for Indigenous health, a broad lens of health that is situated in the context of colonial legacies and the particularities of relationships with the land is required .
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 | 0.002 | 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 | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".