Subnational regional inequality in access to improved drinking water and sanitation in Indonesia: results from the 2015 Indonesian National Socioeconomic Survey (SUSENAS)
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
BACKGROUND: Universal and equitable access to safe and affordable drinking water and adequate sanitation and hygiene in Indonesia are vital to ensure healthy lives and promote well-being for all at all ages. OBJECTIVES: To quantify subnational regional inequality in access to improved drinking water and sanitation in Indonesia. METHODS: Data about access to improved drinking water and sanitation were derived from the 2015 Indonesian National Socioeconomic Survey (SUSENAS) and disaggregated by 510 districts across the 34 provinces of Indonesia. Two summary measures of inequality, mean difference from mean and weighted index of disparity, were calculated to quantify within-province absolute and relative inequality, respectively. RESULTS: While the majority of Indonesian households had access to improved drinking water (71.0%) and sanitation (62.1%), there were large variations between and within provinces. Access to improved drinking water ranged from 93.4% in DKI Jakarta to 41.1% in Bengkulu, and access to improved sanitation ranged from 89.3% in Jakarta to 23.9% in East Nusa Tenggara. Provinces with similar numbers of districts and similar overall averages showed variable levels of absolute and/or relative inequality. Certain districts reported very low levels of access to improved drinking water and/or sanitation. CONCLUSIONS: There are inequalities in access to improved drinking water and sanitation by subnational region in Indonesia. Monitoring within-country inequality in these indicators serves to identify underserved areas, and is useful for developing approaches to improve inequalities in access that can help Indonesia make progress towards the 2030 Agenda for Sustainable Development.
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 | 0.002 | 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 | 0.001 |
| 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