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Record W2874299775 · doi:10.1111/joac.12289

The disappearance of water buffalo from agrarian landscapes in Western China

2018· article· en· W2874299775 on OpenAlexafffund
Jean‐François Rousseau, Janet C. Sturgeon

Bibliographic record

VenueJournal of Agrarian Change · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsSimon Fraser UniversityUniversity of OttawaGlobal Affairs Canada
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLivelihoodModernization theoryChinaAgrarian societyState (computer science)SustainabilityCash cropEthnic groupGeographyLand tenurePolitical scienceEconomic growthEconomyAgricultureEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract For centuries, water buffalo served important livelihood functions for ethnic minority farmers in Southwest China. Yet, over the past decade, buffalo ownership decreased dramatically in our research sites in Yunnan province. This transition occurred after state policies and projects excluded villagers from significant portions of their land. Increased state control over landscapes allowed the state to respatialize land uses in ways conducive to productivist and environmentalist logics, with farmers cultivating cash crops on limited production landscapes and state agencies taking over control of larger environmental landscapes. Buffalo are welcome in neither setting. Handai and Akha farmers have divergent perceptions on the outcomes of these technological shifts, with Handai beginning to question buffalo loss while Akha having contented to become “modern” farmers. In both cases, our analysis challenges the literatures on sustainability transitions and ecological modernization that posit apolitical and optimistic outcomes for farmers' adoption of “modern” and “green” technologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2018
Admission routes2
Has abstractyes

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