Why are so Many Water Points in Nigeria Non-Functional?: An Empirical Analysis of Contributing Factors
Bibliographic record
Abstract
This paper utilizes information from the 2015 Nigeria National Water and Sanitation Survey to identify the extent, timing, as well as reasons for the failure of water points. The paper finds that more than 38 percent of all improved water points are nonfunctional. The results indicate that nearly 27 percent of the water points are likely to fail in the first year of construction, while nearly 40 percent are likely to fail in the long run (after 8-10 years). The paper considers the reasons behind these failures, looking at whether they can or cannot be controlled. During the first year, a water point's location -- the political region and underlying hydrogeology -- has the greatest impact on functionality. Other factors?specifically, those that can be controlled in the design, implementation, and operational stages -- also contribute significantly. As water points age, their likelihood of failure is best predicted by factors that cannot be modified, as well as by the technology used. The paper concludes that, to improve the sustainability of water points, much can be done at the design, implementation, and operational stages. Over time, technology upgrades are important.
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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 | 0.003 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".