Linking Crimes Using Behavioural Clues: Current Levels of Linking Accuracy and Strategies for Moving Forward
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
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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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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