The paradox of water accessibility: understanding the temporal and spatial dimensions of access to improved water sources in Rwanda
Bibliographic record
Abstract
According to the United Nations, the world has met the Millennium Development Goal target of halving the proportion of people without access to safe drinking water. However, global figures mask massive disparities between regions and countries, and within countries. For instance, only 64% of the people in sub-Saharan Africa have access to improved water sources. Over 40% of all people globally who lack access to drinking water live in sub-Saharan Africa. Rwanda is used as a case in point in this study. Despite the abundance of water resources in the country, access to improved water sources is limited. Using the Rwandan Demographic and Health Surveys (2000–2010), we examined regional disparities in access to improved water sources. Results from logistic regression models show that overall, access to improved water has declined between 2000 and 2010; except in the western region, where access to water marginally improved. Educated individuals, wealthier and urban dwellers were more likely to have access to improved water sources over time compared to their uneducated, poor and rural counterparts. The persistence of regional disparities in access to improved water over time suggests the need for policy to address insufficient investments in water infrastructure in Rwanda.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".