Lessons Learned: Overcoming Obstacles to Inference and Synthesis in Atrocity Prevention Research
Bibliographic record
Abstract
In this paper, we argue that atrocity prevention (AP) researchers face obstacles to inference and knowledge synthesis in the study of AP strategies and tools. We argue that researchers can begin to address obstacles to inference by using rigorous social-science methods and can address obstacles to knowledge synthesis through greater coordination and transparency about concepts, methods, and data. Our argument proceeds in four parts. First, drawing on a systematic review of three decades of research about AP tools, we survey key analytic obstacles to drawing conclusions about the effects of AP policy. Second, we survey four separate methods that researchers increasingly use to address some of these inferential issues in individual studies. Third, we survey the subsequent obstacles to synthesizing and aggregating conclusions from these studies, despite methodological advances. We conclude by offering recommendations about how researchers can conduct research that would be easier to synthesize across studies and some initial ideas about how analysts can use the existing body of research to inform policy decisions. In particular, we recommend that both researchers and practitioners adopt a “Bayesian approach” to interpreting evidence from the AP literature by thinking in probabilistic terms, using context-specific information about particular cases to refine estimates of the likely outcomes of AP tools based on more general evidence.
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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 | 0.002 | 0.001 |
| 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".