Criminal Expertise and Sexual Violence: Comparing the Crime-Commission Process Involved in Sexual Burglary and Sexual Robbery
Bibliographic record
Abstract
Criminal expertise relates to the notion that some individuals may develop domain-specific offending skills that differentiate them from those with less skills or experience (i.e., novices). In the expertise literature, burglary has emerged as a distinct type of “expert” offense, therefore the current study sought to determine whether criminal expertise is more evident in the crime-commission process of sexual burglary compared to sexual robbery. We used binary logistic regression to compare the pre-crime, crime, and post-crime behaviors of 870 cases of hybrid sexual assault that occurred during the commission of either a burglary ( N = 319) (or) robbery ( N = 479), both of which involved personal theft from a stranger victim. Findings suggest that the crime commission process of sexual burglary involves a more sophisticated modus operandi and greater expertise in detection avoidance (e.g., strategies to protect their identity and destroying and removing evidence) compared to sexual robbery.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".