Bibliographic record
Abstract
This chapter assesses how the United Nations, in cooperation with the African Union, formed one of the largest and most expensive peacekeeping operations ever deployed to stop the bloodshed in Darfur. The operation took the name United Nations–African Union Mission in Darfur (UNAMID). The United States initiated and orchestrated the most important political aspects that made the deployment of UNAMID possible. At the United Nations, the United States was intimately involved in the drafting and negotiation of UN resolutions pertaining to the Darfur issue and prodded various UN Security Council members to support the respective resolutions. Once UNAMID was approved by the UN Security Council, the United States was deeply involved in recruiting UNAMID participants. Some countries—such as Egypt, China, Canada, and Ethiopia—had a political stake in the Darfur conflict and thus volunteered forces to deploy to Darfur. Nevertheless, the large majority of countries did not join UNAMID on their own initiative. Rather, they were wooed into the coalition by the United States. U.S. officials thereby followed specific practices to recruit these troops. Many of these practices exploited diplomatic embeddedness: U.S. officials used preexisting ties to ascertain the deployment preferences of potential recruits and constructed issue linkages and side payments. The United States was assisted in the UNAMID coalition-building process by UN staff, most notably from the UN Department of Peacekeeping Operations (UNDPKO).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".