The Trichan takedown: Lessons in the governance and regulation of child sexual abuse material
Bibliographic record
Abstract
Abstract Amidst renewed concern about the prevalence of online child sexual abuse material, the global technology sector is refocusing on models of multistakeholder governance and the development of new technological solutions. This paper argues that the language of multistakeholderism and technological solutionism obscures the administrative and commercial practices that facilitate the widespread distribution of abuse material. To illustrate this point, the paper describes the 2019 intervention of the Canadian Centre for Child Protection in the operations of “Trichan”, three websites that were amongst the largest purveyors of abuse material on the open web for 7 years. The case study underscores the materiality of the Internet and the role of commercial relations within the infrastructure stack in the provision of illegal content. While identifying opportunities for the mass removal of abuse material, the paper questions the discretion granted to technology companies under laissez faire regulation, and troubles characterizations of Internet infrastructure as neutral and instrumental factors in the epidemic availability of abuse material.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".