Large weather and conflict effects on internal displacement in Somalia with little evidence of feedback onto conflict
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extreme weather and conflict may drive forced displacement. However, their individual contribution to displacement is not fully understood due to challenges around isolating individual channels of causality. Here, we use novel disaggregated data on internal displacement in all of Somalia’s subregions from 2016 to 2018 broken down by reported reason of displacement and combine it with weather and conflict data. This allows us to isolate the effects of extreme weather and conflict on forced displacement, as well as the effects of displacement on conflict itself. We find large non-linear effects of weather on displacement where an increase in temperature anomalies from 1 °C to 2 °C (to approx. 1.5 standard deviations, SD) leads to a tenfold increase in displaced people, and a reduction in precipitation from 50 mm to 0 mm (approx. 1.5SD) leads to around a fourfold increase in displacement. We find significant effects of conflict events on displacement (which are masked when the data is aggregated) with a 1.5 standard deviation increase in conflict events increasing displacement 50-fold. We further show that displacement itself has little detectable effect on the occurrence of conflict events.
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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 | 0.000 | 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) | 0.000 | 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 it