Collective and Organic Farming in Tamil Nadu: Women’s Participation, Empowerment and Food Sovereignty
Bibliographic record
Abstract
The aim of this paper is to explore the notion of women’s participation, empowerment and food sovereignty among the marginalized women farmers in the state of Tamil Nadu in India. The women farmers who belong largely to the so-called lower castes have been marginalized due to the persistent presence of the patriarchal structure and the continued oppression and discrimination in a caste-ridden society. This is supported and supplemented by the policies and politics of globalization through the state apparatus. This research, based on the fieldwork method, highlights the hitherto undermined role of women farmers in the wake of their efforts at establishing enhanced and sustainable socio-economic relations in connection with the local agricultural land which accounts for their economic and social independence and sovereignty, especially food sovereignty. The process marking this transformation includes collective and organic farming based on millets leading to the creation of an inherent and integral food sovereignty vis-a-vis the increasing usurpation of agricultural land through the nexus of the state government and private companies. The paper also analyzes the issue of land ownership, litigation cases involving women, and the role of community organizations which impel the hitherto marginalized women towards self-sustainable, self-sufficient and self-governed environment in rural agricultural economy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".