Subnational regional inequality in access to improved drinking water and sanitation in Indonesia: results from the 2015 Indonesian National Socioeconomic Survey (SUSENAS)
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".