Receiver operating characteristic curves in the crime linkage context: Benefits, limitations, and recommendations
Bibliographic record
Abstract
Abstract Deciding whether two crimes have been committed by the same offender or different offenders is an important investigative task. Crime linkage researchers commonly use receiver operating characteristic (ROC) analysis to assess the accuracy of linkage decisions. Accuracy metrics derived from ROC analysis—such as the area under the curve (AUC)—offer certain advantages, but also have limitations. This paper describes the benefits that crime linkage researchers attribute to the AUC. We also discuss several limitations in crime linkage papers that rely on the AUC. We end by presenting suggestions for researchers who use ROC analysis to report on crime linkage. These suggestions aim to enhance the information presented to readers, derive more meaningful conclusions from analyses, and propose more informed recommendations for practitioners involved in crime linkage tasks. Our reflections may also benefit researchers from other areas of psychology who use ROC analysis in a wide range of prediction tasks.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".