How we can protect the world's most vulnerable countries against climate shocks
Bibliographic record
Abstract
Extreme weather events and other climate change-linked disasters have devastated communities globally: be it cyclones along the coast of Southern Africa, flooding in parts of Canada, drought-induced wildfires in California, or the recent El Niño (ENSO) induced drought in Eastern and Southern Africa that affected 60 million people. These powerful events trigger humanitarian disasters and wreak economic havoc. They also raise an important question: How can we increase resilience to climate-induced shocks – particularly in poorer countries that are most vulnerable? Our new research, Building Resilience to Climate Shocks in Ethiopia, looks in detail into this question with a focus on the 2015/16 ENSO event that led to erratic rains, causing crop failure, spikes in food insecurity and acute undernutrition. While ENSO is a recurring climate pattern involving changes in the temperature of waters in the central and eastern Pacific Ocean, the 2015/16 event was particularly strong. As a consequence of prolonged drought, 10 million Ethiopians required emergency food aid or other assistance on top of the 8 million already participating in Ethiopia’s social protection program.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".