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Record W2802947902 · doi:10.1111/joac.12268

The political ecology of rice intensification in south India: Putting SRI in its places

2018· article· en· W2802947902 on OpenAlex
Marcus Taylor, Suhas Bhasme

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Agrarian Change · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLivelihoodScope (computer science)Food securityPolitical ecologyPoliticsSouth asiaSystem of Rice IntensificationPaddy fieldBusinessEcologyEconomic growthPolitical scienceDevelopment economicsAgricultureSociologyEconomicsBiology

Abstract

fetched live from OpenAlex

Abstract The system of rice intensification (SRI) has been promoted across Asia as a means to improve rice yields while decreasing water use and external inputs. It is argued to be a generalisable means by which to revalidate smallholder livelihoods and improve food security across the region. Current debates about SRI, however, remain predominantly technical in scope, focusing on field‐level outcomes. To more adequately understand the potential of SRI for smallholder farmers, we argue that it is necessary to situate SRI within a political ecology framework that addresses how the adoption and practice of SRI is shaped by uneven access to key assets including labour, water, and extension networks. Fieldwork conducted in Mahabubnagar district in Telangana, south India—where SRI had been widely disadopted despite the achievement of higher yields—is used to illustrate why agronomic analysis must engage directly with the complex social contexts in which farmers operate.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.069

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.066
GPT teacher head0.265
Teacher spread0.199 · 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