Why Was Benghazi “Saved,” but Sinjar Allowed to Be Lost? New Failures of Genocide Prevention, 2007–2015
Bibliographic record
Abstract
In this article, I examine legal, political, and cultural reasons behind the genocides in Iraq and Syria of 2007–2015, that decimated the Yezidi communities of Sinjar or Shingal (Şengal/Şingal/Şingar). It is typically argued that failures to prevent genocide occur due to imaginative deficits or fear of a military quagmire. However, I show that atrocities are quickly recognized and sanctioned in some cases, and that substantial resources in terms of international support, military assets, and political rhetoric have been generated in several cases in which groups were less threatened than the Yezidis. To explain the disparate responses to claims of imminent persecution or massacre, I develop the theory of the “Reverse CNN Effect,” in which some tragedies do not receive the requisite attention of the mass media to mobilize action. The phenomenon extends beyond the media to the resolutions and reports of the United Nations and, at times, those of the US government.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".