Bibliographic record
Abstract
The article presents a comparative analysis of genocide in Rwanda and Darfur. The first half of the article examines the patterns and origins of violence in both cases and uses the comparison to generate some theoretical inferences about the causes of genocide. The analysis finds that both cases demonstrate a similar character of violence but that in Rwanda the violence was more intense, more exterminatory, and more participatory than in Darfur. Both episodes took place in the midst of civil war, in periods of political transition, in countries with histories of ethnic nationalism, and in areas where the conflicting ethnic populations lived in relative proximity. However, in Rwanda the state is more compact, centralized, and effective, which may explain the variation in intensity. The second half of the article focuses on the international response to genocide in both cases. After Rwanda, observers emphasized the importance of using the label ‘‘genocide’’ and creating domestic constituencies. Darfur showed that both strategies are insufficient. In response to Darfur, US officials declared ‘‘genocide’’ to be occurring, and there emerged a politically diverse civil-society coalition to lobby the administration. Yet the net outcome for both cases, in terms of the absence of an effective policy to halt genocide, was the same. The article argues that focusing too intently on a ‘‘genocide’’ determination may be counterproductive, that international politics matter yet mobilization on Darfur outside of North America was weak, and that protocols for the use of force to prevent genocide should be clarified.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".