Flow My FE the Vendor Said: Exploring Violent and Fraudulent Resource Exchanges on Cryptomarkets for Illicit Drugs
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
A growing share of illicit drug distribution takes place using cryptomarkets that use encryption and anonymization technologies. The risks of law enforcement intervention and violence are lower here than in off-line traditional drug markets, but with the technological innovations follow new opportunities for stealing and fraud. The sites themselves fall prey to theft and hacking attempts, administrators abscond with users’ funds, and malicious sellers regularly cheat buyers. In this study, we explore the types of theft and fraud that occur on cryptomarkets using multiple data sources: formalized community resources (e.g., guides, tutorials), ethnographic observations of user forums, thematic identification of forum posts using unsupervised text classification, and an expert interview. We find system-based violent predatory resource exchange similar to robberies and process-based fraudulent resource exchange similar to rip-offs. We discuss these offenses conceptually as extensions of common drug-related crimes in the digital world. This contributes to the research on how cryptomarkets work and can improve crime-prevention efforts.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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