The Course of Case Linkage Never Did Run Smooth: A New Investigation to Tackle the Behavioural Changes in Serial Car Theft
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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