Lethal Outcome in Elderly Sexual Violence: Escalation or Different Intent?
Bibliographic record
Abstract
Purpose: The study examines violent sexual offenses against elderly victims that resulted in either serious injuries or death and explore whether certain components of the crime-commission process explain the different outcomes. The study investigates the question of whether a lethal outcome in elderly sexual assaults is the result of an escalation in violence or a different intent. Methods: Bivariate and logistic regression analyses were conducted on a sample of 199 offenders convicted of a violent sexual offense against an elderly woman that resulted in either severe physical injury (n = 145) or death (n = 54) of the victim. Results: Results showed that violent sexual offenses ending with the death of the victim were more likely to be characterized by the use of a weapon, foreign object insertion, and the taking of items belonging to the victim. Violent sexual offenses ending with serious physical injuries were characterized by the presence of penetration (anally and vaginally) as well as ejaculation in or on the victim. Conclusions: Differences observed between the two groups suggest that offenders who killed the victims had the intent do so compared to those offenders who inflicted several physical injuries. Practical implications of the findings 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".