Go Bayes or Go Home: Algorithms for Improving Predictive Methods of Police Decision Support
Bibliographic record
Abstract
This thesis tests novel methods of creating advice to assist police with behavioural aspects of investigations. Using a sample of 361 serial stranger sexual offenses, simulated samples, and a sample of 84 serial burglary offences, the paper predicts behavioural characteristics using frequency information and a cross-validation approach. Experiment 1 predicts dichotomous offender characteristics from dichotomous and categorical crime scene characteristics. Experiment 2 predicts continuous behavioural variables from point estimates. Novel Bayesian algorithms are compared to base rate, mean, and point estimate prediction methods. In Experiment 1, Bayes’ Theorem (74.6% accurate) predicts with 11.1% more accuracy than base rates (63.5% accurate), and provides improved advising estimates. In Experiment 2, Bayesian algorithms predict more accurately than mean and point estimate methods (this improvement is not always statistically significant). These tests suggest that Bayesian approaches increase predictive power. Advising statements are considered, and suggestions regarding future research for police decision support are discussed.
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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 | 0.016 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".