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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".