Improving Intervention Decisions to Prevent Genocide: Less Muddle, More Structure
Bibliographic record
Abstract
Decisions to intervene in a foreign country to prevent genocide and mass atrocities are among the most challenging and controversial choices facing national leaders. Drawing on techniques from decision analysis, psychology, and negotiation analysis, we propose a structured approach to these difficult choices that can provide policy makers with additional insight, consistency, efficiency, and defensibility. We propose the use of a values-based framework to clarify the key elements of these complex choices and to provide a consistent structure for comparison of the likely benefits, risks, and tradeoffs associated with alternative intervention strategies. Results from a workshop involving Ambassadors and experienced policy makers provide a first test of this new method for clarifying intervention choices. A decision-aiding framework is shown to improve the clarity and relevance of intervention deliberations, laying the groundwork for a more comprehensive and clearer understanding of the threats and opportunities associated with various intervention options.
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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 | 0.001 | 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".