Multi-level analysis of access to drinking water in rural communes in the south of the Kaffrine region, Senegal
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In Senegal, there are disparities in access to drinking water at several levels. There are major differences between urban and rural areas. In rural areas, poverty, the abundance or scarcity of water infrastructure, and the distance between the place of residence and the place of supply are, to some extent, factors in the disparity in rates of access to drinking water from one village to another and, within the same village, from one household to another. Determining the sources of variance between villages and households regarding access to drinking water is based on the identification of several explanatory variables, both at the aggregate level (village level) and at the individual level (household level). Multilevel analysis has shown that differences in household access to drinking water are due to several factors that can be grouped into two categories: contextual variables that vary from one village to another, and individual characteristics that differ from one household to another. The aim of this article is to analyze the factors that explain the disparity in access to drinking water in rural communes in the south of the Kaffrine region. The usefulness of multilevel analysis lies in its ability to solidify causal inference in the associations between the infrastructural levels of villages, in terms of water facilities, and their impact on the level of access to drinking water of rural households as reported (Bringe and Golaz in Manuel pratique d’analyse multiniveau, Ined Éditions, Aubervilliers, 2017), as reported (Diane et al., in Analyse multiniveau pour expliquer la prévalence d’impacts sanitaires néfastes autorapportés et l’adaptation lorsqu’il fait très chaud et humide en été dans les secteurs les plus défavorisés des neuf villes les plus populeuses du Québec en 2011, 2015).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it