Humanitarian Missions to the Nuba Mountains, Sudan: Delivery of Food to Those in Critical Need
Bibliographic record
Abstract
The following reports delineate the most recent experiences and insights gleaned by Samuel Totten, a scholar of genocide studies based in the United States, as he traveled up into the war-torn Nuba Mountains in Sudan during December 2014 and April–May 2015. During the course of both trips, accompanied by an interpreter and a driver, both from the Nuba Mountains, he served as a witness to the ongoing aerial attacks by the government of Sudan against Nuba civilians (in their villages, on their farms, in open marketplaces, in their schools, and in places of worship) and delivered food (sorghum, lentils, dried beans, salt, sugar, and cooking oil) to those Nuba in the most dire need. An untold number of Nuba have been forced out of their villages and off their farms due to the aerial bombings, and without access to their farms and stores of food, many, particularly those residing in the remotest regions, are experiencing everything from malnutrition to severe malnutrition to starvation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".