Replication Data for: Learning from Intermittent Water Supply Schedules: Visualizing Equality, Equity and Hydraulic Capacity in Bengaluru and Delhi, India
Bibliographic record
Abstract
Intermittent Schedule Vizualization David Meyer 2022-10-24 The repository contains the data, code, and figures associated with the paper Meyer et al. 2023. Over a billion people get water from water supply networks that regularly interrupt their service. Unfortunately, the intermittent operations induce inequalities within the network. In our work, we created the tools needed to use Water Supply Schedules to quantify and compare the inequality of water supply schedules between and within cities. We apply these tools to the largest and most complicated intermittent water systems ever described in peer-reviewed papers: Delhi and Bangalore; they supply water to 25 million people according to 3278 schedules. Uniquely, we propose to use publicly posted water supply schedules to estimate service quality and service equality within intermittent systems at a novel scale: the supply schedule scale. While we demonstrate and visualize the use of supply schedules in Delhi and Bangalore, the implications of our work are much broader: we showcase a new scale at which intermittent systems could and should be researched and regulated and we provide the methods required to do so. Research into intermittent water supplies is often limited by data availability. Against this trend, we openly share our digitized versions of each city’s water supply schedules and the code required to process them in this repository. In doing so, we hope to enable other researchers to explore and model the operations of intermittent systems in ways that reflect the complexity and inequalities of intermittent supply. In this repository, you’ll find the: 1. original schedule data from both cities 2. manually transcribed schedule data 3. processed schedules in long and wide formats 4. code to process the transcribed schedules 5. visual summaries of the schedules 6. code to generate these visual summaries We extended our analysis by intersecting schedule census data using GIS. We include: 7. Census data 8. Our intersected data 9. Code to generate visual summaries of the equity data Relevant files are contained in Data, Code, and Figure subfolders.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".