Decrypting the cryptomarkets: Trends over a decade of the Dark Web drug trade
Bibliographic record
Abstract
Introduction The Dark Web is a subsection of the Internet only accessible through specific search engines, making it impossible to trace users. Due to extensive anonymity, the drug trade on the Dark Web makes regulation complicated. We sought to uncover the scope of the online drug trade on the Dark Web and the impact it may have on the dynamics of global drug trafficking. Methods We conducted a literature review to elucidate the availability and distribution of drugs on the Dark Web based on data reported in existing literature ( n = 14) between September 2012 and June 2019. We simultaneously collected data about substances and listings from Dark Web cryptomarkets ( n = 13) active between August 2022 and January 2023. Data from the literature review and the Dark Web scrape were combined to draw trends in the chronological availability and distribution of drugs between 2012 and 2023. Results The data collected from 13 cryptomarkets between late 2022 and early 2023 showed a relative change in substance distribution compared to 2012–2019, with a decrease in prescription drugs (from >20% to <5%) and a doubling of opioid listings (from 5.5% to 9.25%), while no major changes were observed on average during 2012–2019 according to literature. Conclusions The Dark Web warrants more attention in the analysis of the global drug trade. Understanding Dark Web drug markets can inform targeted interventions and strategies to reduce drug-related harms, while ongoing research is necessary to anticipate and respond to future changes in the landscape of the illicit drug trade.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".