Bayes and Base Rates: What Is an Informative Prior for Actuarial Violence Risk Assessment?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 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 it