MétaCan
Menu
Back to cohort
Record W3049388586 · doi:10.1111/cag.12642

Examining Indigenous perspectives on the health implications of large‐scale agriculture in Jalisco, Mexico

2020· article· en· W3049388586 on OpenAlexfundvenueno aff
A. C. L. Day, Claudia Rocío Magaña‐González, Kathi Wilson

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsIndigenousAgricultureContext (archaeology)GeographyEconomic growthScale (ratio)ColonialismCommunity healthPolitical scienceSocioeconomicsSociologyHealth careEconomicsEcologyArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.199
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207