The Neurobiological Origins of Pedophilia: Not That Simple
Bibliographic record
Abstract
We have read with utmost interest the invited commentary recently published in the Journal of Sexual Medicine entitled “Toward a Neurodevelopmental Understanding of Pedophilia.”1 The search for the cause of pedophilia is important, both for prevention and treatment purposes. However, from our clinical and research experience, Dr. Fazio’s conclusion that “we are slowly moving toward a neurodevelopmental and potentially epigenetic explanation of pedophilic sexual preference” appears premature and only in part supported by the literature she cited. The purpose of our comment is to provide readers of the Journal of Sexual Medicine not specialized in assessment and/or treatment of pedophilia with further information to offer a diverging perspective. We appreciated Dr. Fazio pointing out the major problem in most research on neurobiological correlates of pedophilia conducted thus far, citing one of our publications: “Investigators […] have since concluded that most of these differences were associated with the propensity to commit offenses rather than to pedophilia itself” (p. 2).2 This important distinction needs to be kept in mind when interpreting any study on pedophilia. Unfortunately, in examining the neuroimaging evidence brought forward by Dr. Fazio for her conclusions, one finds that exactly this differentiation is lacking or ignored in the studies cited.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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".