How we can protect the world's most vulnerable countries against climate shocks
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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