Taking Stock of Criminal Profiling
Bibliographic record
Abstract
The use of criminal profiling (CP) in criminal investigations has continued to increase despite scant empirical evidence that it is effective. To take stock of the CP field, a narrative review and a 2-part meta-analysis of the published CP literature were conducted. Narrative review results suggest that the CP literature rests largely on commonsense justifications. Results from the 1st meta-analysis indicate that self-labeled profiler/experienced-investigator groups did not outperform comparison groups in predicting offenders' cognitive processes, physical attributes, offense behaviors, or social habits and history, although they were marginally better at predicting overall offender characteristics. Results of the 2nd meta-analysis indicate that self-labeled profilers were not significantly better at predicting offense behaviors, but outperformed comparison groups when predicting overall offender characteristics, cognitive processes, physical attributes, and social history and habits. Methodological shortcomings of the data and the implications of these findings for the practical utility of CP 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 | 0.101 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".