Differentiation of Homicidal Child Molesters, Nonhomicidal Child Molesters, and Nonoffenders by Phallometry
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the ability of phallometry to discriminate among homicidal child molesters, nonhomicidal child molesters, and a comparison group of nonoffenders. METHOD: Twenty-seven child molesters who had committed or had attempted a sexually motivated homicide, 189 nonhomicidal child molesters, and 47 nonoffenders were compared on demographic variables and psychometrically determined responses to aural descriptions of sexual vignettes. Two phallometric indexes were used: the pedophile index and the pedophile assault index. The pedophile index was computed by dividing the subject's highest response to an aural description of sex with a "consenting" child by his highest response to description of sex with a consenting adult. The pedophile assault index was computed by dividing the subject's highest response to an aural description of assault involving a child victim by his highest response to description of sex with a "consenting" child. RESULTS: Homicidal child molesters, nonhomicidal child molesters, and nonoffenders were not significantly different in age or IQ. Homicidal and nonhomicidal child molesters had significantly higher pedophile index scores than nonoffenders. Significantly more homicidal child molesters (14 [52%] of 27) and nonhomicidal child molesters (82 [46%] of 180) than nonoffenders (13 [28%] of 47) had pedophile index scores equal to or greater than 1.0, but homicidal and nonhomicidal child molesters did not differ from each other. Significantly more homicidal child molesters (17 [63%] of 27) than either nonhomicidal child molesters (71 [40%] of 178) or nonoffenders (17 [36%] of 47) had pedophile assault index scores equal to or greater than 1.0, and nonhomicidal child molesters and nonoffenders were not significantly different from each other. Within-group analyses revealed that of the three groups, only the nonhomicidal child molesters exhibited a significant difference between their pedophile index scores and their pedophile assault index scores; their pedophile index scores were higher. CONCLUSIONS: Consistent with past research, the authors found that the pedophile index is useful in differentiating homicidal and nonhomicidal child molesters from nonoffenders and that the pedophile assault index is able to differentiate homicidal child molesters from nonhomicidal child molesters and nonoffenders.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".