Access to drinking water: time matters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the reported achievement of the Millennium Development Goals (MDGs) with respect to drinking water, lack of access to water remains widespread worldwide. The indicator used there to measure access to water in the MDGs refers to the use of an improved water source. However, the amount of time spent in collecting water is high in countries where access to drinking water supplies located on premises is not common. 26.3% of the world's population did not have such access in 2015. Thus the need to travel to a water point, possibly queue, fill water containers, and carry them home is prevalent. The amount of time and effort used in water collection can be considerable, and household surveys increasingly provide data on collection time. This study aims to demonstrate the effect of adding a 30-minute collection time component to monitor access to drinking water. This study draws on household surveys from 17 countries to highlight the widespread burden of fetching water and its significant impact on estimates of coverage. The proportion of the population with access decreased by 13% on average for these 17 countries when collection time was added as a consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it