Bibliographic record
Abstract
Preventing genocide is more achievable than ever before. In the past two decades, there has been an upsurge in research and resources dedicated to genocide and mass atrocity prevention. There is now substantial knowledge of risk factors for genocide, of current countries at risk of genocide and mass atrocities, and, most importantly, a small but passionate field of practitioners dedicated to prevention. Crucial to their effectiveness, however, is the adoption of evidence-based strategies. One important methodological approach to identifying proven strategies is through comparative analysis of historical case studies of resilience to genocide. From these case studies, in which a demonstrable risk of genocide was offset through effective resilience, researchers can identify cross-situational factors that have proven to reduce risk in the past, and therefore have a high potential to do so again. Adopting this approach, this article examines two cases of extraordinary resilience to genocide—those of Bulgaria and Denmark during the Holocaust. Through careful examination of these case studies, three factors important for promoting resilience can be identified. These include the role of leaders in contributing to prevention; the importance of early and robust condemnation of persecution in changing the trajectory of each crisis; and the importance of discursive space in which to challenge narratives of oppression. Each of these factors offers new insights for evidence-based approaches to genocide prevention.
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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 | 0.110 | 0.196 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".