Mobilizing the will to intervene : leadership & action to prevent mass atrocities
Bibliographic record
Abstract
The Will to Intervene (W2I) Project vi Without leadership from the Prime Minister of Canada and the President of the United States, our countries will make little progress toward solving the recurring global problems of mass atrocities and their lethal ripple effects.We lay out missed policy options that Canada and the U.S. could have pursued in Rwanda in 1994, and describe successful responses to early warning in Kosovo.By providing detailed case studies of Canadian and U.S. decision making over Rwanda and Kosovo, W2I aims to help decision makers envisage innovative and timely solutions in the future.The introduction to this report, Part I, describes the impacts of genocide and mass atrocities, highlighting the enormous security, financial, and political costs of inaction.Our introductory section also analyzes the emerging drivers of deadly violence in the 21 st century.Part II, the most important section of the report, presents our policy recommendations in four thematic sections devoted to the generation of domestic political will.Part III of the report consists of the W2I historical case studies analyzing the Canadian and American decision making process concerning the 1994 Rwandan Genocide and the 1999 Kosovo crisis.In addition to research on current responses to mass atrocities, the case study analyses in Part III provide the basis for the development of the policy recommendations in Part II.Part IV consists of the appendices, which include the selected bibliography for the case studies, the list of interviewees, biographies of the W2I Project's Co-Directors and researchers, members of the Research Steering Committee and Academic Consultation Group, as well as a list of acronyms.part one: Introduction "Political will is not something you find if you look in the right cupboard.It has to be laboriously crafted, case by case, using the resources of both insiders and outsiders, bottom up from civil society and through peer group pressure from those in positions of influence nationally and internationally.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".