The political ecology of rice intensification in south India: Putting SRI in its places
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".