Climate variability, armed conflicts and child malnutrition in sub-saharan Africa: A spatial analysis in Ethiopia, Kenya and Nigeria
Bibliographic record
Abstract
Background: Sub-Saharan Africa (SSA) has one of the highest prevalence of malnutrition among children under 5 in the world. It is also the region most vulnerable to the adverse effect of climate change, and the one that records the most armed conflicts. The chains of causality suggested in the literature on the relationship between climate change, armed conflict, and malnutrition have rarely been supported by empirical evidence for SSA countries. Methods: This study proposes to highlight, under the hypothesis of spatial non-stationarity, the influence of climatic variations and armed conflicts on malnutrition in children under 5 in Ethiopia, Kenya, and Nigeria. To do this, we use spatial analysis on data from Demographic and Health Surveys (DHS), Uppsala Conflict Data Program Georeferenced Event Dataset (UCDP GED), Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Results: The results show that there is a spatial autocorrelation of malnutrition measured by the prevalence of underweight children in the three countries. Also, local geographically weighted analysis shows that armed conflict, temperature and rainfall are positively associated with the prevalence of underweight children in localities of Somali in Ethiopia, Mandera and Turkana of Wajir in Kenya, Borno and Yobe in Nigeria. Conclusion: In conclusion, the results of our spatial analysis support the implementation of conflict-sensitive climate change adaptation strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".