Mobilizing the Will to Intervene
Bibliographic record
Abstract
Despite the handwringing and promises of "never again," the grim recurrences of genocide and crimes against humanity around the world have made it emphatically clear that the international community has been largely ineffective in stopping mass atrocity crimes. Drawing on candid interviews with eighty key figures involved in American and Canadian responses to the Rwandan genocide of 1994 and the Kosovo crisis of 1999, Mobilizing the Will to Intervene explains why and provides a roadmap for change. Since appeals to the "moral law" carry little weight in the political calculations of modern states, the authors argue that civil society must persuade governments that the prevention of mass atrocities around the world is in every country's national interest. In a globalized world, violence, disease, and instability triggered by mass atrocities in one place affect the security, health, and prosperity of all other regions. No nation is an island. Impassioned, insightful, and determined, Mobilizing the Will to Intervene is a direct appeal to American and Canadian politicians, NGOs, journalists, and the public to participate effectively in the prevention of mass atrocities by pressuring their leaders to act. With simple, practical recommendations, this book shows how civil society can participate in preventing future mass atrocities and help repair a ruined system of international aid.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".