Differential household vulnerability to climatic and non-climatic stressors in semi-arid areas of Mali, West Africa
Bibliographic record
Abstract
Semi-Arid Regions (SARs) of West Africa are considered climate change “hotspots” where strong ecological, economic and social impacts converge to make socio-ecological systems particularly vulnerable. While both climatic and non-climatic drivers interact across scales to influence vulnerability, traditionally, this inter-connectedness has received little attention in vulnerability assessments in the region. This study adopted the vulnerability patterns framework, operationalized using the Multidimensional Livelihood Vulnerability approach to include both climatic and non-climatic stressors to analyze differential household vulnerability in SARs of Mali. Findings showed that while drought was the most mentioned climate-related stressor, households were also exposed to a diversity of environmental and socio-economic stressors, including food scarcity, livestock disease, labour unavailability, crop damage, and erratic rainfall patterns. The typology revealed three vulnerability archetypes differentiated by adaptive capacity and sensitivity. Availability of productive household members, household resource endowments, livelihood diversification and social networks were the main discriminant factors of household adaptive capacity, while challenges relating to food and water security make households more sensitive to stressors. The analysis highlighted the heterogeneity in household vulnerability patterns within and across communities. Failing to account for this heterogeneity in adaptation planning might result in a mismatch between adaptation needs and interventions, and potentially in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".