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Record W2120054506 · doi:10.1093/cdj/bsp024

Subaltern strategies and autonomous community building: a critical analysis of the network organization of sustainable agriculture initiatives in Andhra Pradesh

2009· article· en· W2120054506 on OpenAlexfundno aff
Ashok Kumbamu

Bibliographic record

VenueCommunity Development Journal · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsSubalternCommodificationSubsistence agriculturePeasantHegemonyPoliticsSociologyGlobalizationAgricultureEconomic growthPolitical economyPolitical scienceSocial scienceEconomyEcologyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract This paper examines and analyses the organization and functioning of subaltern peasant sanghams (grassroot associations of the poor) and their place-based as well as network-based strategies in building autonomous local communities that challenge the consequences of neoliberal globalization in general and the commodification of agriculture and food in particular. The major objective of the counter-hegemonic organizational strategies is to build self-protective and subsistence communities, to mend the metabolic rift between nature and society, and to re-reconstruct social fabric within communities. The question remains is whether place-based autonomous communities can sustain in an increasingly globalizing world. To better understand these political dynamics, I use Karl Polanyi's concept of ‘double movement’ and examine the making of a double movement in Indian agriculture and its socio-political and ecological implications for the Indian peasantry. I use the organizational strategies and activities of the Deccan Development Society, a prominent non-governmental organization that has been working in Medak district for more than two decades, as an illustrative case study.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0110.016
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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 designQualitative
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

Citations15
Published2009
Admission routes1
Has abstractyes

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