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Record W2906592349 · doi:10.5038/1911-9933.12.3.1611

New Documents Shed Light: Why Did Peacekeepers Withdraw during Rwanda’s 1994 Genocide?

2018· article· en· W2906592349 on OpenAlexvenueno aff
Emily Willard

Bibliographic record

VenueGenocide Studies and Prevention · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
FundersUniversity of Washington
KeywordsGenocidePeacekeepingMandatePolitical scienceInternational communityGovernment (linguistics)Security councilLawPublic administrationForeign policyPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0080.006
Scholarly communication0.0110.006
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.017
GPT teacher head0.325
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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