Why Livelihoods Matter in the Gendering of Household Water Insecurity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract One of the most pressing and immediate climate concerns globally is inadequate and unsafe household water. The livelihoods of smallholder crop and livestock farmers are especially vulnerable to these challenges. Past research suggests that water insecurity is highly gendered, and women are theorized to be more aware of and impacted by water insecurity than men. Our study reengages this literature through a livelihood lens, comparing gendered perception of household water insecurity across crop and livestock subsistence modalities in a semiarid region of Burkina Faso in the Sahel region of West Africa, where water insecurity is closely intertwined with both seasonality and rainfall unpredictability. Our mixed-methods ethnographic study sampled matched men and women in households with water insecurity data collected from 158 coresident spousal pairs who engaged primarily in pastoralism or agriculture. Contrary to predictions from the existing literature, men engaged in livestock husbandry perceived greater water insecurity than matched women in the same household. We suggest this reflects men’s responsibility for securing water for the animals—which consume most of the household’s water among livestock farmers. In contrast, men engaged in cropping perceive less water insecurity than women in the same household, aligning with predictions from past research. Our findings suggest that the relationship between gender and water insecurity is more highly nuanced and related to livelihood strategies than previously recognized, with significant implications for how water insecurity is conceptualized theoretically and methodologically in the contexts of people’s everyday management and experience of the most immediate and proximate climate-related challenges.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it