Gender and Resilience to Climate Variability in Pastoralists Livelihoods System: Two Case Studies in Kenya
Bibliographic record
Abstract
Recurrent droughts due to climate change has led to vulnerability of the pastoralist communities, leading to loss of assets and food insecurity. Climate change will have different impacts on women and men’s livelihoods. Building resilience at individual, household and community level will largely depend on the suitability of interventions to the local context, particularly in relation to the social dynamics and power relations that create differences in vulnerability. Most of the research have focused on national and regional studies. The impact of climate change will not be uniformly distributed in countries within Africa or within the same country. This specific research focuses on two diverse ecological zones at the local level in the same County of Turkana in north western Kenya: agro-pastoral zone and primary pastoral zone. This paper aims to evaluate women and men’s adaptive capacity to climate variability in Turkana, north-western Kenya. It is evident that increasing resilience can be realised by reducing vulnerabilities and increasing adaptive capacity. The results revealed that agro-pastoralists are more resilient to climate change than primary pastoralists. Male headed household are more resilient than female headed households. Access to basic services is contributing more in the resilience score than assets, gender of house hold head and age. Generally, few families in this region have very high resilience score.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".