Variations in household water affordability and water insecurity: An intersectional perspective from 18 low- and middle-income countries
Bibliographic record
Abstract
Compounding systems of marginalization differentiate and shape water-related risks. Yet, quantitative water security scholarship rarely assesses such risks through intersectionality, a paradigm that conceptualizes and examines racial, gendered, class, and other oppressions as interdependent. Using an intersectionality approach, we analyze the relationships between household head gender and self-reported socio-economic status, and water affordability (proportion of monthly income spent on water) and water insecurity (a composite measure of 11 self-reported experiences) for over 4000 households across 18 low- and middle-income countries in Central and South America, Africa, and Asia. Interaction terms and composite categorical variables were included in regression models, adjusting for putative confounders. Among households with a high socio-economic status, the proportion of monthly income spent on water differed by household head gender. In contrast, greater household water insecurity was associated with lower socio-economic status and did not meaningfully vary by the gender of the household head. We contextualize and interpret these experiences through larger systems of power and privilege. Overall, our results provide evidence of broad intersectional patterns from diverse sites, while indicating that their nature and magnitude depend on local contexts. Through a critical reflection on the study's value and limitations, including the operationalization of social contexts across different sites, we propose methodological approaches to advance multi-sited and quantitative intersectional research on water affordability and water insecurity. These approaches include developing scale-appropriate models, analyzing complementarities and differences between site-specific and multi-sited data, collecting data on gendered power relations, and measuring the impacts of household water insecurity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".