Spotlight on Yemen's Forgotten War and Humanitarian Disaster: Preventing the Next Syrian Refugee Crisis
Bibliographic record
Abstract
Yemen’s humanitarian situation is arguably the worst humanitarian crisis in the world and the world is looking the other way. The United Nations reports that Yemen has more people — 21.2 million — in need of humanitarian aid than any other country including Syria. Yemen is in the midst of a civil war and reports of human rights violations are frequent. Millions are on the brink of famine, the country’s health system has collapsed and thousands of civilians have been killed or injured by fighting.International attention and aid funding are desperately needed and could be critical to helping forestall a Yemeni refugee crisis before it begins. However, the crisis in Yemen has been largely under-reported and overshadowed by other conflicts such as Syria. International donors in 2015 provided only half of the estimated US$1.6 billion dollars that the United Nations requested for Yemen, and the window for preventative action is closing. The global community has spent billions reacting to the Syrian refugee crisis. Unless donors act now to address the severity of Yemen’s humanitarian crisis, the cost — both human and financial — will soar much higher.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".