Delays in attentional processing when viewing sexual imagery: The development and comparison of two measures
Bibliographic record
Abstract
Purpose. Critically important to effectively treating and managing sexual offending is the identification or validation of an offender's deviant sexual interests as the nature of their sexual interests is what demarcates repetitive sexual offenders from non‐offenders and lower risk offenders. As an alternative or verification to self‐report or phallometric measures, focus has turned to attention‐based measures. These measures assess sexual content‐induced delay (SCID), a specific form of attentional bias associated with preferred sexual content (images or text). Viewing time (VT) and choice reaction time (CRT) were developed and utilized to assess sexual interest via SCID () and examine the measures’ clinical utility via estimates of sensitivity and specificity. Method. Participants were 44 youth non‐sexual offenders, 60 university students, and 22 adult sexual offenders. Differences between groups were examined on various sub‐scores and receiver operator characteristic curves provided information on clinical utility. Results. The VT and CRT measures produced subtest scores with high reliability in all three samples. There were significant differences in VT between the adult sexual offenders and the youth non‐sexual offenders, but not between the youth non‐sexual offenders and the university students. Some of the VT subtests demonstrated good clinical utility in their ability to differentiate adult heterosexual sexual offenders from non‐sexual offenders (e.g., area under the curve (AUC) = 0.87 female mature images, 0.88 male child images). Interestingly, the VT and CRT measures provided significantly different results. Conclusion. The results of this study provide further evidence that measures of SCID are accurate and are useful as indications of sexual interest. Differences between measures suggest, however, that further work is required.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".