Difference in improved water source adoption between urban and rural households in Cameroon
Bibliographic record
Abstract
Many households living in developing countries still collect water from unimproved sources. The situation is particularly worse in rural areas. This study analyses the differences in improved water source adoption between urban and rural households in Cameroon. Our analysis uses data from the fifth Cameroon Demographic and Health survey conducted in 2018-2019. Results from logit regressions suggest that the use of improved water source increases when the head of household is a woman. It also increases with education, access to information and wealth. Conversely, it decreases with household size. The approach of Fairlie (2006) is further used to evaluate the contribution of the above factors to urban-rural differences in the adoption of improved water sources. Our analysis shows that the above factors explain 41% of the differences in water source choices observed between urban and rural households. The policy recommendations of the research are described in the paper.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".