Linking Crimes Using Behavioural Clues: Current Levels of Linking Accuracy and Strategies for Moving Forward
Bibliographic record
Abstract
Abstract The number of published studies examining crime linkage analysis has grown rapidly over the last decade, to the point where a special issue of this journal has recently been dedicated to the topic. Many of these studies have used a particular measure (the area under the receiver operating characteristic curve, or the AUC) to quantify the degree to which it is possible to link crimes. This article reviews studies that have utilised the AUC and examines how good we are currently at linking crimes (within the context of these research studies) and what factors impact linking accuracy. The results of the review suggest that, in the majority of cases, moderate levels of linking accuracy are achieved. Of the various factors that have been examined that might impact linking accuracy, the three factors that appear to have the most significant impact are crime type, behavioural domain, and jurisdiction. We discuss how generalisable these results are to naturalistic investigative settings. We also highlight some of the important limitations of the linking studies that we reviewed and offer up some strategies for moving this area of research forward. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.358 | 0.645 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".