Child pornography, the Internet and juvenile suspects
Bibliographic record
Abstract
An example of a recurrent issue in communities is the legal system's struggle to process problems of young offenders. The main focus of this article is juvenile suspects of crimes considering child pornography. Two topics are discussed: the nature of these crimes and the characteristics of suspects. An analysis of 159 Dutch police files on child pornography shows that almost a quarter of the suspects are under 24 years of age. Of that group, 35% is younger than 18 years. Often, these are youngsters who take sexualised pictures and/or make videos of themselves and/or each other. If this material is distributed via the Internet it becomes a matter for law enforcement agencies. This conclusion forms the base of the Discussion section of this article. Should law enforcement agencies settle these cases in an informal manner? Or should suspects be prosecuted in order to prevent certain types of child pornography from ceasing to be punishable?
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".