The role of criminal expertise in serial sexual offending: a comparison to “novices”
Bibliographic record
Abstract
Purpose Serial offenders have been described as more forensically aware, better able to control their victim, and ultimately, more adept at eluding detection. Despite these assertions, there is a lack of research examining differences in “criminal expertise” (i.e. offense-related skills and competencies) between serial and non-serial offenders. The purpose of the current study is to address this empirical research gap. Design/methodology/approach The current study uses binary logistic regression to examine a sample of 83 serial offenses and 322 offenses involving “novices” (i.e. offenders without a previous criminal history) to determine whether criminal expertise is a distinctive feature of the crime-commission process of serial offenders, compared to novices. Findings Binary logistic regression findings indicated that offenders who did not verbally reassure their victim, who brought a weapon to the offense and who selected a victim who was walking were more likely to be serial. Taken together, these behaviors do not suggest that serial offenders are “experts” at avoiding detection, but rather, indicate some general offense competencies and skills related to violent offending. Originality/value The current study offers the first direct application of the criminal expertise framework to serial sexual offending. The findings offer new insights for the treatment and management of offenders who possess offense-related competencies and skills, which can offer a complementary view to more deficit-based models.
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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 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".