Bayes and Base Rates: What Is an Informative Prior for Actuarial Violence Risk Assessment?
Bibliographic record
Abstract
Bayes' theorem describes an axiomatic relationship among marginal and conditional proportions within a single "experiment." In many ways, it has been fruitful to greatly extend this idea to the task of drawing inferences from data much more generally. Commonly, what matters is how all prior knowledge is revised (or not) by new findings resulting in posterior (sometimes "subjective") probabilities. And, to address many important problems, it is sensible to conceive of probability in such subjective terms. However, some commentators in the domain of violence risk assessment have assumed an analogous axiomatic relationship among marginals (i.e., priors in the form of base rates) observed in one study and conditionals (i.e., posteriors in the form of revised rates) expected in a separate study or assessment context. We present examples from our own research to suggest this assumption is generally unwarranted and ultimately an unaddressed empirical matter.
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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 | 0.074 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".