Deployment of Troops to Prevent Impending Genocide: A Contemporary Assessment of the UN Security Council’s Powers
Bibliographic record
Abstract
Summary As civil conflicts between ethnic or religious groups have increased in number, the United Nations has developed greater effectiveness in intervening in such conflicts and has made preventive measures a focus of planning and undertakings of the UN system. One obstacle to implementing preventive measures is the problem of national sovereignty. This article looks at the still relatively unused potential of the UN to deploy military troops as a measure to deal with crises of serious magnitude before they erupt into genocide, highlighting both the obstacles posed by state sovereignty and the potential for success. The article offers a comprehensive study of the human rights provisions of the UN Charter to show how they can operate to authorize the UN to take action to prevent impending genocide. Further, Security Council action in southern Rhodesia, northern Iraq, Bosnia, Somalia, Haiti, and Rwanda is examined, both illustrating the potential of early military action and raising questions about the timing of preventive measures. The article concludes that the most important challenge facing the UN is how to improve its capacity to prevent impending genocide. The success of military action in preventing genocide will determine the acceptance of future preventive measures of this nature, as states weigh whether the cost to their sovereignty is reasonable in view of the benefits obtained.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".