The Dynamic Environment of Crypto Markets: The Lifespan of New Psychoactive Substances (NPS) and Vendors Selling NPS
Bibliographic record
Abstract
The Internet has played a major role in the distribution of New Psychoactive Substances (NPS), and crypto markets are increasingly used for the anonymous sale of drugs, including NPS. This study explores the availability of individual NPS and vendors on the crypto markets and considers whether crypto markets are a reliable platform for the sale of NPS. Data was collected from 22 crypto markets that were accessed through the hidden web using the Onion Router (Tor). Data collection took place bimonthly from October 2015 to October 2016 as part of the CASSANDRA (Computer Assisted Solutions for Studying the Availability aNd DistRibution of novel psychoActive substances) project. In seven snapshots over 12 months, 808 unique vendors were found selling 256 unique NPS. The total number of individual NPS and vendors increased across the data collection period (increase of 93.6% and 71.6%, respectively). Only 24% (n = 61) of the total number of NPS and 4% (n = 31) of vendors appeared in every snapshot over the 12 months, whereas 21% (n = 54) of NPS and 45% (n = 365) of vendors only appeared once throughout the data collection. The individual NPS and vendors did not remain the same over the 12 months. However, the availability of NPS and vendors selling NPS grew. NPS consistently available on crypto markets could indicate popular substances.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".