Sexual Abuse of Elderly Victims Investigated by the Police: From Motives to Crime Characteristics
Bibliographic record
Abstract
Elderly sexual abuse has been almost completely ignored from researchers and practitioners alike. However, the occidental population is aging and living longer, suggesting that the number of cases of elderly sexual abuse should increase. Moreover, elderly sexual assaults have been described as being more violent, resulting in more severe injuries, and are more frequently committed by strangers, making criminal investigations more difficult to solve. The current study aims to identify the various motivations associated with elderly sexual abuse and to test whether it is possible to link offender and modus operandi characteristics to these motivations. In other words, the main objective is to identify "why" the elderly are sexually abused, "how," and "by whom"? Using two-step cluster analysis on a sample of 128 cases of extra-familial elderly sexual assaults (aged 65 years or more) from France, four clusters of offenders' motivation were identified. Congruent with previous studies, results showed that elderly sexual abuse was motivated by sex, anger, and opportunities. However, a fourth cluster was identified, describing offenders motivated by experimentation. These offenders, in addition to being young with a lack of criminal experience, were also more likely to perform the most intrusive sexual acts and to use physical violence, sometimes to the point of killing their victim. To test the external validity of our cluster solution, a series of bivariate analyses were conducted. Results showed that the four motivations were also associated with specific offender and crime characteristics. These findings highlight the importance of looking at the motivations underlying elderly sexual abuse to suggest better interventions strategies as well as improve the criminal investigation of these cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".