The United Nations and Genocide Prevention: The Problem of Racial and Religious Bias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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 it