Hidden wholesale: The drug diffusing capacity of online drug cryptomarkets
Bibliographic record
Abstract
BACKGROUND: In spite of globalizing processes 'offline' retail drug markets remain localized and - in recent decades - typically 'closed', in which dealers sell primarily to known customers. We characterize drug cryptomarkets as 'anonymous open' marketplaces that allow the diffusion of drugs across locales. Where cryptomarket customers make stock-sourcing purchases for offline distribution, the cryptomarket may indirectly serve drug users who are not themselves cryptomarket customers, thereby increasing the drug diffusing capacity of these marketplaces. Our research aimed to identify wholesale activity on the first major cryptomarket, Silk Road 1. METHODS: Data were collected 13-15 September 2013. A bespoke web crawler downloaded content from the first major drug cryptomarket, Silk Road 1. This generated data on 1031 vendors and 10,927 drug listings. We estimated monthly revenues to ascertain the relative importance of wholesale priced listings. RESULTS: Wholesale-level revenue generation (sales for listings priced over USD $1000.00) accounted for about a quarter of the revenue generation on SR1 overall. Ecstasy-type drugs dominated wholesale activity on this marketplace, but we also identified substantial wholesale transactions for benzodiazepines and prescription stimulants. Less important, but still generating wholesale revenue, were cocaine, methamphetamine and heroin. Although vendors on the marketplace were located in 41 countries, wholesale activity was confined to only a quarter of these, with China, the Netherlands, Canada and Belgium prominent. CONCLUSIONS: The cryptomarket may function in part as a virtual broker, linking wholesalers with offline retail-level distributors. For drugs like ecstasy, these marketplaces may link vendors in producer countries directly with retail level suppliers. Wholesale activity on cryptomarkets may serve to increase the diffusion of new drugs - and wider range of drugs - in offline drug markets, thereby indirectly serving drug users who are not cryptomarket customers themselves. Cryptomarkets provide researchers and policy makers with a rich source of drug monitoring information. Further research should ascertain whether their virtual location may reduce the violence associated with middle market drug activity. We caution that conflict may instead manifest in other ways, including threats, fraud, and blackmail.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".