Genocide Prevention and Western National Security: The Limitations of Making R2P All About Us
Bibliographic record
Abstract
The case for turning R2P and genocide prevention from principle to practice usually rests on the invocation of moral norms and duties to others. Calls have been made by some analysts to abandon this strategy and “sell” genocide prevention to government by framing it as a matter of our own national interest including our security. Governments’ failure to prevent atrocities abroad, it is argued, imperils western societies at home. If we look at how the genocide prevention-as-national security argument has been made we can see, however, that this position is not entirely convincing. I review two policy reports that make the case for genocide prevention based in part on national security considerations: Preventing Genocide: A Blue Print for U.S. Policymakers (Albright-Cohen Report); and the Will to Intervene Project. I show that both reports are problematic for two reasons: the “widened” traditional security argument advocated by the authors is not fully substantiated by the evidence provided in the reports; and alternate conceptions of security that would seem to support the linking of genocide prevention to western security—securitization and risk and uncertain—do not provide a solid logical foundation for operationalizing R2P. I conclude by considering whether we might appeal instead to another form of self interest, “reputational stakes”, tied to western states’ construction of their own identity as responsible members of the international community.
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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 | 0.073 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.027 | 0.046 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.021 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".