An Examination of the Home-Intruder Sex Offender
Bibliographic record
Abstract
One particular crime location in sexual assaults is the victim's residence. Similar to sexual burglars, "home-intruder" sex offenders choose to assault the victim in her residence, most likely in their bedroom. The aim of the current study is to analyze modus operandi, temporal factors, and victim characteristics in a sample of 347 stranger sexual assaults committed by 69 serial sex offenders to determine which factors may be more relevant to sexual assaults committed in the victim's residence compared with sexual assaults committed at another type of location. Our hypothesis is that offenders who choose to sexually assault victims in their home constitute a specific type of sex offender, one that resembles the sexual burglar. Results showed that modus operandi (e.g., burglary), temporal factors (e.g., time at crime scene with victim), and victim characteristics (e.g., age, victim-offender relationship) were significant in predicting whether the victim encounter, crime site, and victim release site were located at the victim's residence or not. Moreover, these findings were generally significant across the three crime locations, which can be explained by the high consistency in location during home-intrusion sexual assaults. Situational crime prevention strategies aimed at making a residence less attractive for offenders should help reducing this particular type of sexual assault.
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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 | 0.002 |
| 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) | 0.002 | 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".