Leveraging a Multi-Method Approach to Improve Mass Atrocity Forecasting
Bibliographic record
Abstract
Forecasting mass atrocities is a central concern for academics, policymakers, and practitioners, but determining where and when mass atrocities will occur is far from straightforward. Over the last few decades, researchers around the world have developed several forecasting models. Some of these models—like the Political Instability Task Force model or the Australia Forecasting Project model—have emphasized quantitative assessments of the risk of mass atrocity. Others—like the UN Framework of Analysis for Atrocity Crimes—have focused on how case-specific factors coalesce to impact the onset of mass atrocity. In this article, we suggest that a multi-methods framework that capitalizes on the strengths of each of these approaches enhances the ability to correctly forecast mass atrocities. Specifically, we rely upon case-based analysis that identifies combinations of factors that are associated with the absence of atrocities as well as two quantitative approaches geared toward predicting the onset of mass atrocity. After integrating results, we assess how well the forecasts fare and discuss the possible uses of our multi-methods approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".