The Course of Case Linkage Never Did Run Smooth: A New Investigation to Tackle the Behavioural Changes in Serial Car Theft
Bibliographic record
Abstract
Abstract This study aimed to investigate the case linkage principles, behavioural consistency and distinctiveness, with a sample of serial car thieves. Target selection, acquisition, and disposal behaviours, as well as geographical and temporal behaviours, were examined. The effects of temporal proximity and offender expertise were also investigated as moderating factors of behavioural consistency. As in previous case linkage research, geographical and some target selection behaviours were able to predict whether crime pairs are linked or unlinked at a statistically significant level. Crucially, it was also found that temporal behaviours demonstrate a significant capability to predict linkage status, a variable which has never before been applied to the prediction of linkage in serial car theft. Furthermore, it was demonstrated that changing the operationalisation of the behavioural domains can affect the results obtained. No support was found for the moderation of behavioural consistency on the basis of temporal proximity or expertise. Overall, the results support previous case linkage studies, furthering their practical applicability within the criminal justice system. Copyright © 2012 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".