Assessing Sexual Arousal with Adolescent Males Who Have Offended Sexually: Self-Report and Unobtrusively Measured Viewing Time
Bibliographic record
Abstract
Sexual arousal was assessed using three approaches: the Affinity (Version. 1.0) computerized assessment of unobtrusively measured viewing time (VT), Affinity self-report ratings of sexual attractiveness, and a self-report sexual arousal graphing procedure. Data were collected from 78 males, aged 12-18 (M = 15.09; SD = 1.62), who acknowledged their sexual assaults. The pattern of responses to all three assessment techniques was remarkably similar, with maximal sexual interest demonstrated and reported for adolescent and adult females. Both self-report procedures could significantly distinguish those adolescents who assaulted a child from those who assaulted peers or adults. The self-report procedures could also significantly discriminate those adolescents with male child victims. The Affinity VT approach significantly differentiated those adolescents who assaulted male children from those who assaulted other individuals. No assessment technique could accurately identify those adolescents with exclusively female child victims. Overall, the results suggest that structured, self-report data regarding sexual interests can be useful in the assessment of adolescents who have offended sexually.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".