Evaluating the Competing Claims on the Role of Ownership Regime Models on International Drinking Water Coverage
Bibliographic record
Abstract
While progress has been made for providing drinking water through the completion of the Millennium Development Goals and other international programs, millions of people still do not have access to clean drinking water. This study examines how drinking water coverage is impacted using three regime ownership models. Using the framework of the privately-owned, publicly-owned, and decentralized regime models, the impacts of water production, non-revenue water, and unit operation cost are evaluated for drinking water coverage. A sample of 144 utilities across 33 countries were sampled using data from the International Benchmarking Network for Water and Sanitation Utilities. Using ordinary least squares modeling, results indicate that predicting water coverage from water production, non-revenue water, and unit operational costs provided weak explanations of variation for both publicly-owned and decentralized regimes. None of the three regime models established a significant relationship between water coverage and all three independent variables. For publicly- and privately-owned water regimes, decreasing non-revenue water by plugging leaks and improving infrastructure can translate into higher rates of water coverage. For decentralized water regimes, higher levels of unit operational cost can increase water coverage. The regression analyses also showed that broad claims about regime ownership, efficiency, and improved water coverage should be suspect. None of the three regime models established a significant relationship between water coverage and all three independent variables. This suggests that the competing claims that privatized drinking water utilities as being more efficient or more able to provide water coverage as compared to other types of utilities in the literature is not supported when compared across countries.
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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 | 0.049 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".