A heuristic study of the similarities and differences in offender characteristics across potential and successful serial sexual homicide offenders
Bibliographic record
Abstract
This heuristic study examined potential serial sexual homicide offenders (SSHOs), an unacknowledged offender group comprised of aspiring and probable SSHOs, and compared them with successful SSHOs. Data were collected on six aspiring SSHOs who each failed a single homicide attempt, 16 probable SSHOs who committed 17 homicides in separate events, and 13 successful SSHOs who killed 90 victims in separate events. The study results indicate that while potential SSHOs share more in common with successful SSHOs than they do with single-victim nonsexual homicide offenders, and that there is an overlap between potential SSHOs and successful SSHOs, there is currently insufficient evidence to suggest that there are discreet transitions among categories. While few potential SSHOs strive to become successful SSHOs, this may be due to weak or nonexistent emotional triggers. Being a potential SSHO does not appear to be a predictable first step on a pathway towards becoming a successful SSHO, as potential SSHOs cannot reliably be thought of as prospective SSHOs if all things were equal. The present study could not foresee all potential SSHOs becoming successful ones. An as yet unidentified number of factors still appear to separate potential SSHOs from successful SSHOs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".