The United Nations and Genocide Prevention: The Problem of Racial and Religious Bias
Bibliographic record
Abstract
Could racial or religious bias within the United Nations be hindering efforts to prevent and punish the crime of genocide? I answer this question by surveying the UN response to a variety of alleged genocides, ranging from Biafra starting in the late 1960s to Syria starting in 2012. In terms of quantitative analysis, this article explores whether the UN response to claims of genocide is proportionate to the scale of actual harm, using absolute death tolls and percentage reductions in the populations of specific minority groups to assess harm. It finds that voting blocs based on racial or religious identity may be warping the UN response to potential genocides, resulting in disproportionate attention across cases. In this regard, the Arab League, the Non-Aligned Movement, and the Republic of Turkey appear to play important roles in shaping UN responses. In terms of qualitative analysis, the article surveys evidence that key actors at the United Nations may have been motivated by bias in framing collective responses to claims of genocide and other mass violence.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".