Behavioural Profiles and Offender Characteristics across 111 Korean Sexual Assaults
Bibliographic record
Abstract
Abstract Although the potential usefulness of the offence action–offender characteristic (A–C) relationships is widely accepted and operational ‘offender profiling’ units now exist around the world, few such relationships have been empirically established. To explore this, the offending action patterns within 111 sexual assault cases from South Korea were coded in terms of 16 distinctive, objective crime scene criteria and subjected to an agglomerative hierarchical cluster analysis. Background psychiatric and general characteristics, Personality Assessment Inventory (PAI) scale scores, and criminal histories were described for each cluster. The cluster analysis drew attention to six clusters or behavioural profiles within the sexual assaults. Cluster 1 included serial offenders who aggressively raped and robbed adult women, with some pseudo‐intimate sexual behaviour, in their homes. Two thirds of these offenders had histories of sexual assault. Cluster 2 included offenders who again targeted adults in their homes, but without pseudo‐intimate sexual behaviour. Cluster 3 included offenders who targeted adults outdoors at night. These offenders showed high antisocial personality PAI scores and psychiatric histories of sexual sadism. Cluster 4 included unarmed offenders who targeted adults in their homes without robbery. These offenders often had psychiatric histories of depression. Cluster 5 included offenders who targeted adults outdoors with a blitz‐style attack, and Cluster 6 included offenders who targeted minors outdoors, without weapons, using a confidence‐trick style of approach. Paedophilia and histories of psychiatric treatment were prominent amongst these offenders. The results indicate therefore some of the key empirical relationships that future research may develop as the basis for sexual assault ‘profiles’. Copyright © 2014 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".