Maintaining groundwater collection over the rainy season with water ATM price reductions: a study in Kitui County, Kenya
Bibliographic record
Abstract
ABSTRACT Rainy seasons across rural sub-Saharan Africa see a dramatic reduction in the collection of groundwater from water points, exposing communities to health risks and reducing sustainability of service providers. Kenyan water service provider FundiFix operates water points in dispersed rural communities in semi-arid Kitui County and sees five to ten times less water collected, and revenues close to zero, during rainy seasons. Water ATMs record precise volumes of water dispensed and allow for timely price changes. It was hypothesised that reducing price from 3 KES to 1 KES per jerrycan would cheaply maintain clean water collection and possibly increase revenue. FundiFix tested this intervention over the March-April-May 2023 rainy season at three water ATM piped schemes and communicated the price reduction to users, with a fourth control unchanged. This did little to nothing to maintain the collection of water at dry season levels. This shows other practitioners that to address the seasonality challenge price reductions need to be combined with deeper understanding of user behaviour, which requires further study. This study only cost 100 USD in lost revenue from reduced price. Implications for practitioners are outlined. Conditional transfers of water credit to users, rather than price reductions, are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".