The utility of latent variable models in refining behavioural crime scene analysis of serial stranger sexual offences
Bibliographic record
Abstract
Behavioural crime scene analysis (BCSA) is a police tool used to reconstruct an offence based on behaviours. Recently, BCSA has demonstrated clinical utility by predicting recidivism and aiding case conceptualization. However, a systematic review of BCSA models showed a paucity of research evaluating which behaviours are necessary and sufficient to model sexual offences. Groth and Birnbaum’s sex offender typology, which is based on offence behaviours, provides a theoretical framework that integrates investigative information and clinical practice. The purpose of this thesis was to evaluate statistical- and theory-based approaches to refine BCSA models that distinguish sexual offenders. In Studies 1 through 3, Multidimensional Scaling, Nonlinear Principal Component Analysis, and Latent Class Analysis were used to create statistically-driven and theory-driven behavioural models from 59 serial, stranger sexual offenders. Validity testing of the theory-driven model indicated that applying Groth and Birnbaum’s framework to BCSA could optimize both investigative efforts and clinical decision-making.
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.
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 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".