Finding the Key Players in Online Child Exploitation Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The growth of the Internet has been paralleled with a similar growth in online child exploitation. Since completely shutting down child exploitation websites is difficult (or arguably impossible), the goal must be to find the most efficient way of identifying the key targets and then to apprehend them. Traditionally, online investigations have been manual and centered on images. However, we argue that target prioritization needs to take more than just images into consideration, and that the investigating process needs to become more systematic. Drawing from a web crawler we specifically designed for extracting child exploitation website networks, this study 1) examines the structure of ten child exploitation networks and compares it to a control group of sports‐related websites, and 2) provides a measure (network capital) that allows for identifying the most important targets for law enforcement purposes among our sample of websites. Results show that network capital — a combination between severity of content (images, videos, and text) and connectivity (links to other websites) — is a more reliable measure of target prioritization than more traditional measures of network centrality taken alone. Policy implications 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.
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 it