Paying Lip Service to R2P and Genocide Prevention: The Muted Response of the US Atrocities Prevention Board and the USHMM’s Committee on Conscience to the Crisis in the Nuba Mountains
Bibliographic record
Abstract
This article addresses the response of the United States Atrocities Prevention Board and the United States Holocaust Memorial Museum’s Committee on Conscience to the ongoing crisis in Sudan’s Nuba Mountains (2011–present). First, it provides an overview of the genocide by attrition of the Nuba people perpetrated by the government of Sudan during the late 1980s and early to mid-1990s; second, it delineates the causes and impact of the current crisis in the same region; and, third, it discusses the parallels and differences between the two sets of events. It then examines how, and speculates as to why, the responses of both the Atrocities Prevention Board and the Committee on Conscience have been largely nonexistent and sorely ineffectual.
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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 | 0.008 | 0.011 |
| 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 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".