Conveying intermittent water supply schedules digitally: production burdens and consumption possibilities in Coimbatore, India
Bibliographic record
Abstract
Abstract Intermittently available piped water imposes unequal mental, physical, and financial stresses on a billion water users worldwide. These burdens lighten when water is supplied according to a reliable and accessible supply schedule. Little academic consideration has been given to the institutional and technical processes that shape how schedules are produced and conveyed; these processes are particularly influential where schedules are updated frequently (e.g. daily). This paper investigates the production-side burdens and uneven consumption benefits of digital schedule conveyance in Coimbatore, India, where the utility posted daily water supply schedules online in 2022 as part of digital governance reforms. We used a mixed-methods approach, combining interviews with utility staff, content analysis of user engagement on X/Twitter, and data extracted from schedule documents. Bureaucratic workflows, limited digital training, operators’ precarity, and political interference hindered the production of timely, accurate, standardized, and useful schedules. Daily schedule production required an average of 69 h of labor from 128 personnel (>70% valve operators). Posted schedules were inconsistently formatted, making them difficult for residents to interpret. X/Twitter data analysis validated usability and accuracy concerns, while highlighting limited attempts by residents to engage with schedules. Ambiguous locality names, missing or irregular timing information, and non-machine-readable formats impeded the computation of supply metrics. Digital schedule conveyance can improve transparency and reduce residents’ stress, but only if utilities invest in institutional capacity-building, data standards, and user-centered design. Utilities should compensate and train staff and disseminate machine-readable schedules (with uniquely identified localities and consistently reported supply times) through multiple channels. With reform, digitally conveyed water supply schedules could advance user-centric, equity-focused, and data-driven urban water governance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".