A pilot development of virtual stimuli depicting affective dispositions for penile plethysmography assessment of sex offenders
Bibliographic record
Abstract
There are concerns regarding the reliability, realism, and validity of stimulus materials used in the assessment of sexual interests among sex offenders. This article explores new stimulus materials for use with penile plethysmography (PPG) assessments. First, this paper presents a pilot study where undergraduate students rated virtual characters (male and female) on perceived age. In addition, the materials developed are unique in that they depict the characters exhibiting varying affective dispositions, including neutral, fearful, sad, joyful, and seductive. Participants in the first study were also asked to identify the affective disposition of the virtual characters, and results suggest that affective disposition was largely perceived as intended, especially in terms of identifying the general emotional valence of the affective dispositions (i.e., positive versus negative). In a second pilot study, we used the computer-generated images to measure sexual arousal responses in a group of non-deviant males recruited in the community. Responses measured through penile plethysmography suggest participants responded to the stimuli as expected, as the greatest amount of sexual arousal was recorded when participants were shown the adult female character. In addition, participants responded with significant arousal only when the adult female character was depicted as sexually open (joyful or seductive), rather than sexually closed or neutral. Results suggest these materials may discriminate sexual interests if applied within clinical forensic assessment of sex offenders.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".