A 'Drought-Free' Maharashtra? Politicising Water Conservation for Rain-Dependent Agriculture
Bibliographic record
Abstract
Soil moisture conservation ('green water') and runoff capture ('blue water') can reduce agricultural risks to rainfall variation. However, little is known about how such conjoined initiatives articulate with social inequity when up-scaled into formal government programmes. In 2014, the Government of Maharashtra institutionalised an integrative green-blue water conservation campaign to make 5000 new villages drought-free each year (2015-2019). This paper analyses the extent to which the campaign, Jalyukt Shivar Abhiyan, enhanced the capture, equity, and sustainability of water for agricultural risk reduction. We find government interests to demonstrate villages as 'drought-free' affected the character and implementation of this integrative campaign. First, drainage-line and waterbody initiatives were disproportionately implemented over land-based adaptations to redress water scarcity. Second, initiatives were concentrated on public land – and less so on agricultural plots – to achieve drought-free targets. Third, the campaign conflated raising overall village water availability with improvements in water access. These dynamics: 1) limited the potential impact of water conservation; 2) excluded residents, including members of historically disadvantaged groups, who did not possess the key endowments and entitlements needed to acquire the benefits associated with drought-relief initiatives; and 3) fuelled additional groundwater extraction, undermining water conservation efforts. Villages will not be drought-free unless water conservation benefits are widespread, accessible, and long-term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".