Child sexual exploitation material offenses: differences in individual and case characteristics based on how they came to attention of police
Bibliographic record
Abstract
There is global demand for methods to prioritize child sexual exploitation material (CSEM) investigations. Previous research comparing online CSEM offenders based on how they were detected found potentially meaningful differences in offense and individual characteristics, including factors relating to targets for prioritization, such as risk of other offending. The present study builds on this work by providing an in-depth comparison of the individual characteristics and offending behavior of a sample of 336 men convicted of CSEM offenses, divided into four detection groups: (1) those reported by others; (2) those identified during another police investigation; (3) those identified due to their online web purchases or downloads, and; (4) those detected during proactive online police investigations. As a group, the riskiest individuals were detected by reports of others and during other investigations (Cohen’s f = .25). This finding suggests that it is important to search for CSEM when doing other police investigations, particularly those involving allegations of sexual offending or crimes against children. Risk relevant information may also assist prioritization, though it will depend on the information available at different points in an investigation and may require the use of professional judgement in approximating evidence of robust risk factors.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".