The multifaceted relationship between extreme weather events, displacement and conflict: New insights from Somalia
Bibliographic record
Abstract
Extreme weather and migratory events have been topics of great interest for decades. More recently, a debate has emerged whether the human impact of climate change can lead to armed conflict, and how conflict and extreme climate events interact in inducing large-scale displacement. This paper explores the relationship between conflict and displacement in the context of droughts and floods across Somalia, a country in the East Africa region, in which the population has historically been using migration as coping strategy for the effects of recurring climatic extremes and socio-economic uncertainties. Since 2015, Somalia has been affected by a humanitarian crisis, paired with on-going conflict, which subsequently reduced resilience of its population. Applying panel econometric methods to monthly within-country migration observations from 18 regions together with spatio-temporal conflict and weather data, this paper quantifies the impacts of conflict and extreme weather events on within-country displacement over the period of 2016 to 2018. It combines and analyses conflict-, drought- and flood-related displacement data from the UNHCR-led Protection & Return Monitoring Network (PRMN), disaggregated conflict data from the Armed Conflict Location & Event Data Project (ACLED) with gridded climate estimate data. Empirical evidence suggests significant interaction effects between conflict and extreme weather events on migration, where pre-existing conflict conditions act as accelerators of climate-induced displacement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".