Bibliographic record
Abstract
It is no accident that Canadian General Roméo Dallaire, the commander of the un military forces in Rwanda during the 1994 genocide, volunteered to co-direct the Will to Intervene (w2i) Project, or that his partner in formulating the study was Frank Chalk, the director of the Montreal Institute for Genocide and Human Rights Studies (migs) at Concordia University and a long-time history professor who lost many relatives during the Holocaust.For those of us who work on the responsibility to protect civilians from mass atrocity crimes -and I proudly count myself in their number after having been the research director for the International Commission on Intervention and State Sovereignty (iciss) -the tragic absence of political will to stop perpetrators of genocide, crimes against humanity, ethnic cleansing, and war crimes is an old story.It is often lamented but too rarely confronted.Standing at the very heart of this tragedy is the story of General Dallaire, denied the troop reinforcements and the freedom to act that he requested to stop the Rwandan Genocide of 1994.Thoughtfully and constructively, Mobilizing the Will to Inter vene: Leadership to Prevent Mass Atrocities seeks to break the cycle of indifference and confront the political will not to intervene that was so painfully evident in 1994.Drawing on incisive case studies contrasting American and Canadian government responses to the Rwandan Genocide and events in Kosovo in
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.304 | 0.228 |
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".