Child sexual abuse material on the darknet
Bibliographic record
Abstract
Abstract By routing traffic through a random combination of servers worldwide, the darknet obfuscates the identity of its users, making it an attractive medium for journalists, dissidents, and individuals committing crimes. Since 2008, access to the darknet has been facilitated by the The Onion Router (TOR) browser, bringing the darknet within reach of an increasingly wider audience. Tens of thousands of darknet forums serve the criminal needs of millions of users each day and hundreds of these darknet forums are especially dedicated to the exchange of child sexual abuse materials (CSAM). Practitioners who work with men with sexual offences may therefore face individuals whose sexual offences occurred partly or wholly in the darknet. In the current review article, we summarize both the scientific literature and evidence obtained from CSAM forum “take-downs,” to describe the organization of darknet CSAM forums and the activities of their members. These forums report large and international memberships of individuals who, much like mainstream social media, interact online on a regular basis, creating large, online communities in which like-minded individuals can socialize and barter CSAM with minimal risk of discovery. Not all forum members contribute equally to the community, and especially administrators appear indispensable for the proper functioning of the CSAM forum. Implications for future research and law enforcement are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".