Expert Versus Novice: Criminal Expertise in Sexual Burglary and Sexual Robbery
Bibliographic record
Abstract
Although there has been considerable variation in the application of expertise to offending populations, one aspect that is widely agreed upon is that expertise is best represented on a continuum from novice to expert. The present study, therefore, investigated criminal expertise in 877 hybrid offenses that involve sexual assault and robbery (i.e., sexual robbery) or burglary (i.e., sexual burglary). Specifically, we analyzed the crime-commission processes of both these offenses using latent class analyses to determine the heterogeneity of criminal expertise among each domain. Results showed an expert to novice continuum in both domains, including a "domain-specific" expert sexual burglary subgroup who was characterized by a high degree of offense-related competencies relevant to sexual burglary. We also found an expert subgroup in sexual robbery who had more general skills (i.e., overlapping expertise) relevant to violent offending. Implications for offender decision-making, treatment, and practice are discussed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".