Flow My FE the Vendor Said: Exploring Violent and Fraudulent Resource Exchanges on Cryptomarkets for Illicit Drugs
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.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".