Predictors of access to safe drinking water: policy implications
Bibliographic record
Abstract
Abstract Nearly one-quarter of the world's population lacks effective access to safe drinking water (SDW). The discovery and implementation of affordable and workable measures to supply safe affordable drinking water internationally remains elusive. Few works have examined a range of economic, institutional, and governance factors influencing that access. To address these gaps in the literature, the current study investigates the role of selected economic, demographic, and hydrologic characteristics as well as institutional and governance indicators, all of which could contribute to explaining access to SDW internationally. It estimates regression models based on data from 74 countries for the period 2012–2017. Results contribute to our understanding of factors that are significant at influencing access to SDW. Results show that demographic, economic, size of the public sector, governance, and educational factors all play important roles. Surprisingly, the avoidance of high levels of corruption and the protection of high levels of civil liberties reveal weaker-than-expected effects. Results carry important implications for informing choices facing communities who seek economically affordable measures to provide access to safe affordable drinking water.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".