DRUG MARKETS AND ANONYMIZING TECHNOLOGIES
Bibliographic record
Abstract
Online drug markets taking advantage of social media and encryption software (e.g. Tor network) and cryptocurrencies (e.g. Bitcoin, Monero) to conceal the identity and physical location of their users are a relatively new area of internet research. Yet, a range of socio-technical innovations have contributed to the proliferation of drug markets on the Internet. Due to the illegality of drugs and drug dealing are anonymizing technologies regarded as important socio-technical practices among its participants allowing to mitigate risks of vendors and customers when exchanging drugs. This panel draws together a number of leading scholars in this emerging area of research to explore questions and issues associated with online platforms enabling illicit transactions. The collection of papers in this panel contribute empirical data and theoretical insight on a range of relevant topics in the study of online drug markets, including methodological challenges, social embeddedness, trust production and governance on cryptomarkets. Various papers in this panel propose new concepts for understanding cryptomarkets as social phenomena where relationships enable economic transactions. It also pluralizes trust building on online platforms and, expanding it from merely institution-based mechanisms to include social relations such as interpreting signs and signals or previous interactions between buyers and sellers. They also expand on reliability of data gathered via anonymous online interviews, drawing attention to participation of marginalized communities. The aim of this panel is to bring together new research to further our understanding of the overall impact of online platform emergence upon global drug markets and to better model their impact on drug dealing, online networks and society in general.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".