A conceptual framework for gender and climate mainstreaming to mitigate water inaccessibility in rural<scp>sub‐Saharan</scp>Africa
Bibliographic record
Abstract
Abstract Evidence underscores that water inaccessibility in rural sub‐Saharan Africa (SSA) disproportionately affects women due to patriarchal gender norms and practices. In the context of Sustainable Development Goals 5 (Gender Equality) and 6 (Water and Sanitation), globally driven efforts and initiatives are set against a backdrop of empowering women, improving rural water accessibility, and reducing water‐related risks. Furthermore, climate change is altering spatiotemporal patterns of water availability and quality. A thematic analysis of the literature was conducted through Scopus and Web of Science to identify drivers and consequences of as well as coping strategies for water (in)accessibility in rural SSA. A conceptual framework was developed to better understand and assess research gaps and points of intervention for gender and climate mainstreaming in mitigation strategies that reduce the impacts of water inaccessibility in rural SSA. Findings show that complex intersecting factors underlie water inaccessibility—and responses—among rural women in SSA. The complex socio‐ecological interlinkages among climate change, water, and gender are discussed and a case is made for more integrative research (including dimensions of vulnerability, impacts, and effective grassroots strategies and co‐benefits) to inform policy, planning, and practice. This article is categorized under: Engineering Water > Water, Health, and Sanitation Science of Water > Water and Environmental Change Human Water > Rights to Water
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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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".