Gender identities, water insecurity, and risk: Re‐theorizing the connections for a gender‐inclusive toolkit for water insecurity research
Bibliographic record
Abstract
Abstract Informed by decades of literature, water interventions increasingly deploy “gender‐sensitive” or even “gender transformative” approaches that seek to redress the disproportionate harms women face from water insecurity. These efforts recognize the role of gendered social norms and unequal power relations but often focus narrowly on the differences and dynamics between cisgender (cis) men and women. This approach renders less visible the ways that living with water insecurity can differentially affect all individuals through the dynamics of gender, sexuality, and linked intersecting identities. Here, we first share a conceptual toolkit that explains gender as fluid, negotiated, and diverse beyond the cis‐binary. Using this as a starting point, we then review what is known and can be theorized from current literature, identifying limited observations from water‐insecure communities to identify examples of contexts where gendered mechanisms (such as social norms) differentiate experiences of water insecurity, such as elevating risks of social stigma, physical harm, or psychological distress. We then apply this approach to consider expanded ways to include transgender, non‐binary, and gender and sexual diversity to deepen, nuance and expand key thematics and approaches for water insecurity research. Reconceptualizing gender in these ways widens theoretical possibilities, changes how we collect data, and imagines new possibilities for effective and just water interventions. This article is categorized under: Human Water > Value of Water Engineering Water > Water, Health, and Sanitation Human Water > Water as Imagined and Represented Human Water > Methods
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.099 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".