The Screening Scale for Pedophilic Interests (SSPI): Construct, predictive, and incremental validity.
Bibliographic record
Abstract
This study of 410 adult male sex offenders against children, using data from the Dynamic Supervision Project (Hanson, Harris, Scott, & Helmus, 2007), examined the construct, predictive, and incremental validity of the Screening Scale for Pedophilic Interests (SSPI; Seto & Lalumière, 2001), a brief proxy measure of phallometrically assessed sexual response to children that is based on sexual victim characteristics. As predicted, the SSPI was significantly related to the Deviant Sexual Interests item on the STABLE-2007 (Hanson et al., 2007), a dynamic risk measure encompassing multiple domains, and with the Deviant Sexual Interests item from its predecessor, the STABLE-2000 (Hanson et al., 2007). The SSPI was unrelated (or more weakly related) to items measuring general antisociality. In addition, the SSPI significantly predicted sexual recidivism, defined as new charges or convictions for sexual offenses, and a broader sexual recidivism outcome that included breaches of community supervision conditions that might involve sexually motivated behavior (e.g., being in the presence of children unsupervised). The SSPI did not add to the predictive accuracy of 2 actuarial risk measures, the Static-99R and Static-200R (Helmus, Thornton, Hanson, & Babchishin, 2012), but it did add to the predictive accuracy of the STABLE-2007. Additional analyses suggest the SSPI can serve as a substitute for the STABLE-2007 Deviant Sexual Interests item, if necessary (e.g., in archival research), when assessing sexual offenders against children.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".