Selling Drugs on Darkweb Cryptomarkets: Differentiated Pathways, Risks and Rewards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cryptomarkets, anonymous online markets where illicit drugs are exchanged, have operated since 2011, yet there is a dearth of knowledge on why people use these platforms to sell drugs, with only one previous study involving interviews with this novel group. Based on 13 interviews with this hard to reach population, and data analysis critically framed from perspectives of economic calculation, the seductions of crime, and drift and techniques of neutralization, we examine the differentiated motivations for cryptomarket selling. Throughout the interviews, we observe an appreciation for the gentrified norms of cryptomarkets and conclude that cryptomarket sellers are motivated by concerns of risks and material rewards, as well as non-material attractions in a variety of ways that both correspond with, and differ from, existing theories of drug selling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it