Assessing Water Poverty of Livelihood Groups in Peri-Urban Areas around Dhaka under a Changing Environment
Bibliographic record
Abstract
Water poverty, measured by the Water Poverty Index (WPI), is traditionally applied at country and community levels. This study presents a livelihood-inclusive approach for measuring WPI at the livelihood group level. The specific objectives are to evaluate present and future WPIs for different livelihood groups, such as large and small male farmers, female farmers, male and female industrial workers and economically inactive women. Primary data are collected from three peri-urban areas around Dhaka using a mixed approach, including a semi-structured questionnaire survey of 260 respondents. The WPIs are calculated by using a weighted multiplicative function, and the component weights are assigned by principal component analysis. The results show that the economically inactive women are presently the most water-poor group, with a WPI value of 41, whereas the small male farmers would be the most water-poor group in the future, with a WPI value of 34. Environmental changes, such as high temperature, variability in rainfall and surface water, lowering of groundwater level, rapid population growth and unplanned urbanization, are found to be responsible for the dynamism in WPIs for different livelihood groups. The Resource and Environment components should be paid immediate attention in order to protect peri-urban livelihood groups from future water poverty.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".