Victims’ Routine Activities and Sex Offenders’ Target Selection Scripts: A Latent Class Analysis
Bibliographic record
Abstract
This study investigates target selection scripts of 72 serial sex offenders who have committed a total of 361 sex crimes on stranger victims. Using latent class analysis, three target selection scripts were identified based on the victim's activities prior to the crime, each presenting two different tracks: (1) the Home script, which includes the (a) intrusion track and the (b) invited track, (2) the Outdoor script, which includes the (a) noncoercive track and the (b) coercive track, and (3) the Social script, which includes the (a) onsite track and the (b) off-site track. The scripts identified appeared to be used by both sexual aggressors of children and sexual aggressors of adults. In addition, a high proportion of crime switching was found among the identified scripts, with half of the 72 offenders switching scripts at least once. The theoretical relevance of these target selection scripts and their practical implications for situational crime prevention strategies 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".