Going Local on a Global Platform
Bibliographic record
Abstract
Objective: There is broad agreement in the literature on the transformative potential of drug cryptomarkets that allow sourcing on a global market and consequently the circumvention of existing supply chains between producer and end user. We examine whether the transformative potential of drug cryptomarkets has been realized in two ways: Are cryptomarket drug sellers found in production and transit countries? and Do we see the increased use of shipping across international borders over time? Method: Using data collected by the DATACRYPTO software tool between 2013 and 2016, we characterize cryptomarket buyer behavior through the product reviews (i.e., sales transactions) posted on 15 cryptomarkets. Findings: Cryptomarket drug sellers are predominantly based in countries of Europe, North America, and Oceania. For both cannabis resin and cocaine sold on cryptomarkets, we find that known production and transit countries are not the primary sources of supplied drugs but rather key countries of consumption. In the case of 3,4-methylenedioxymethamphetamine, we observe that the Netherlands, a known production country, is the largest supplier. We further observe tendencies over time toward increased localization of cryptomarkets with regard to product destinations. Discussion: Though cryptomarkets offer a potentially global platform for drug distribution, they do not tend to be used as such. We explain our results with reference to buyers’ preferences regarding safety, risk, and convenience, alongside structural limitations for cryptomarket use such as bitcoin availability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".