Examining the Role of Similarity Coefficients and the Value of Behavioural Themes in Attempts to Link Serial Arson Offences
Bibliographic record
Abstract
Abstract When relying on crime scene behaviours to link serial crimes, linking accuracy may be influenced by the measure used to assess across‐crime similarity and the types of behaviours included in the analysis. To examine these issues, the present study compared the level of linking accuracy achieved by using the simple matching index (S) to that of the commonly used Jaccard's coefficient (J) across themes of arson behaviour. The data consisted of 42 crime scene behaviours, separated into three behavioural themes, which were exhibited by 37 offenders across 114 solved arsons. The results of logistic regression and receiver operating characteristic analysis indicate that, with the exception of one theme where S was more effective than J at discriminating between linked and unlinked crimes, no significant differences emerged between the two similarity measures. In addition, our results suggest that thematically unrelated behaviours can be used to link crimes with the same degree of accuracy as thematically related behaviours, potentially calling into the question the importance of theme‐based approaches to behavioural linkage analysis. 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 | 0.045 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".