New Documents Shed Light: Why Did Peacekeepers Withdraw during Rwanda’s 1994 Genocide?
Bibliographic record
Abstract
Why did the international community decide to withdraw United Nations peacekeeping troops from Rwanda during the 1994 genocide? Analysis of newly released documents and results from an international conference with former U.N. and government officials sheds further light on our understanding of what took place leading up to and during the Rwandan genocide. This article focuses on two key moments: 1) the United States’ reluctance to support the peacekeeping mission from before its mandate began and prior to the killing of U.S. troops in Somalia in autumn 1993; and the United States’ central role pushing the United Nations Security Council to call for a withdrawal of UNAMIR. It provides a greater understanding of international decision making in the U.N. security council, as well as foreign policy making within the U.S. government, contributing to more effective genocide prevention policy and advocacy efforts.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".