Untapped transformative potential in decentralised groundwater systems that improve households' resilience in India's water-scarce urban environments
Bibliographic record
Abstract
Off-grid, decentralised water infrastructures are increasingly being promoted to help cities meet their water delivery targets and build resilient water systems in the face of changing water availability. Against this background, this paper examines how integrating off-grid, decentralised groundwater systems with centralized piped supply helps water utilities and urbanites achieve transformative resilience under conditions of chronic water scarcity in the secondary city of Tiruppur, India. Empirical findings on users' water access practices from off-grid systems like municipal or private borewells to remain resilient, their role in the everyday governance of these systems, and the costs of building resilience through borewells—obtained through a participatory action research project where 94 households recorded month-long water access using a ‘water and waste calendar’—reveal that Tiruppur's public borewells are an affordable and mostly inclusive resilience building measure but are far from being transformative. Moreover, diverse arrangements for their everyday governance unevenly enable user participation, impose invisible costs on female members of borewell-reliant households, and create differentiation in service quality across the city. The paper argues that the potential to achieve transformative resilience through off-grid sources like borewells remains untapped in Tiruppur and concludes with some reflections to pursue just and inclusive resilience.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".