Climate adaptation and WASH behavior change in the Lake Victoria Basin
Bibliographic record
Abstract
Abstract As climate change disrupts the global hydrological cycle, bringing extremes of flooding and drought, many communities will experience changes in water and sanitation quality and access, requiring adaptive behavior changes. This study set out to map the adaptation patterns – namely, the strategies employed to cope with water, sanitation, and hygiene (WASH)-related impacts of climate change – within the Mabinju community, located along the banks of Lake Victoria in Western Kenya. Qualitative methods were employed, involving 17 semi-structured individual interviews and seven focus groups with village members. Insights derived from direct conversations with village members were deepened through qualitative interviews with an additional 13 WASH sector stakeholders working in the wider Lake Victoria Basin region. Through this study, various WASH-specific community adaptation measures were identified, with both positive and negative impacts on long-term local climate resilience. While many positive coping strategies were found to be spurred by the creative faculties of local residents, capacities for adaptation were found to be restrained by broader forces of poverty and resource access, resulting in the adoption of certain maladaptive coping mechanisms. These findings highlight the need for climate adaptation interventions in the WASH sector to simultaneously build on existing resilience-enhancing measures while addressing the root causes of maladaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".