Safely managed drinking water services in the Democratic People’s Republic of Korea: findings from the 2017 Multiple Indicator Cluster Survey
Bibliographic record
Abstract
Abstract Safely managed drinking water services (SMDWS) is the service ladder used for the Sustainable Development Goal (SDG) monitoring of drinking water and expands on the Millennium Development Goal metric (“improved water source”) with three additional criteria, namely: availability when needed, accessibility on premises, and safety (free from faecal and priority chemical contamination). Multiple Indicator Cluster Surveys (MICS) have been used for progress monitoring accounting for a significant fraction of the water, sanitation, and hygiene (WASH) indicator data. In its most recent iteration MICS now includes additional SMDWS indicators. The objective of this study was to report on recent SDG target 6.1 baseline data on SMDWS from the Democratic People’s Republic of Korea gathered from a MICS conducted in 2017. Survey results indicated that 93.7% of the population used an improved drinking water source, but when this was combined with the SDG criteria of water availability, accessibility, and safety, coverage was reduced to 92.3, 78.2, and 74.4%, respectively. This resulted in estimates that 60.9% of the population used a SMDWS. The survey results illustrate how the improved SDG indicators can highlight the required gaps to be overcome with regard to universal and equitable access to SMDWS. Further analysis and discussion regarding water quality deterioration between source and household as well as population residence, wealth group index, geographical distribution, and other characteristics relative to SMDWS indicators are also further analysed and discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".