A critique of current child molester subcategories: a proposal for an alternative approach
Bibliographic record
Abstract
This paper examines the utility of previous attempts to subcategorize child molesters. We argue that research based on these categorizations has resulted in confusion due to differences across studies concerning which offenders belong in each group. For example, there are no agreed-upon guidelines for identifying a child molester as an incest offender or as a ‘stranger’ offender. We examine research findings associated with previous attempts to subcategorize child molesters as well as the literature on modus operandi. From this, we conclude that current attempts to subcategorize child molesters are flawed. We propose that distinguishing child molesters according to new criteria – that is, whether or not they have been previously associated with their victim – should result in more productive research and provide a better guide for treatment and postdischarge supervision. In our terminology, nonaffiliative child molesters are those offenders who are truly strangers to their victims whereas affiliative child molesters are characterized by an established caregiving relationship with the child for some period prior to the offense. These men, unlike nonaffiliative offenders, engage in a protracted grooming process before offending. Finally, we outline research, treatment, and risk-management strategies relevant to each of our subcategories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".