“If I was in charge”: A qualitative investigation of water security, gender-based violence and wellbeing in Kenya
Bibliographic record
Abstract
The links between lack of access to WASH (water, sanitation, hygiene) and adverse health outcomes is well documented. There is a recent nascent literature on the links between water security and gender-based violence (GBV) that is relatively sparse; this is surprising given firstly that the global water issue is quintessentially a gendered one and secondly that we know this to be a major issue for women particularly in Sub Saharan Africa. This paper reports on the lived experiences of seniors through oral histories (n = 25) with a particular focus on WASH and gender-based violence using Kisian, Kenya as a case study. Results reveal concerns due to inadequate access to safe water and sanitation facilities and also perceptions of structural gender-based violence where participants reported feeling marginalized by government due to lack of supply of clean piped water. The results also reveal that women are excluded from water governance. In conclusion, gender mainstreaming in water resource management and financial support for gender equity should be adopted by all relevant actors in the WASH sector, particularly given our learnings from the COVID 19 pandemic.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".