Investigation of Gender-Differentiated Impacts of Water Poverty on Different Livelihood Groups in Peri-Urban Areas around Dhaka, Bangladesh
Bibliographic record
Abstract
The concept of water poverty integrates water stress, access to safe water and sanitation, socioeconomic capacity, water uses and environmental aspects. This study presents a new concept to demonstrate gender-differentiated impacts of water poverty for different livelihood groups in a peri-urban setting. The objective is to identify how gender and livelihood groups are affected by dynamic water poverty. A few peri-urban areas around Dhaka city were selected as the study area, and male and female industrial workers, small-scale male and female farmers, and economically inactive women were the target groups. Data were collected through a questionnaire survey of 220 respondents and using a number of participatory appraisal tools, such as focus group discussions, and semi-structured and key-informant interviews. Water poverty was assessed by its five components: resource, access, capacity, use and environment. Peri-urban water poverty is found not only to be limited to scarcity, or physical availability, of water resources, but also to encompass political, social, economic and institutional dimensions. The study found that inadequate availability and quality of water, restricted access and use, poor socioeconomic capacity and fragile environment affected every livelihood group, but these factors eventually led to extreme impoverishment for adolescent girls, and physically challenged and elderly women. Suggestions are provided as to how gender issues of water can be balanced at different levels to make water management decisions gender-sensitive.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".