Online and offline determinants of drug trafficking across countries via cryptomarkets
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 Drug cryptomarkets are a significant development in the recent history of illicit drug markets. Dealers and buyers can now finalize transactions with people they have never met, who could be located anywhere across the globe. What factors shape the geography of international drug trafficking via these cryptomarkets? In our current study, we test the determinants of drug trafficking through cryptomarkets by using a mix of social network analysis and a new dataset composed of self-reported transactions. Our findings contribute to existing research by demonstrating that a country’s level of technological advancement increases the probability of forming trafficking connections on cryptomarkets. Additionally, we found that a country’s capacity to police cryptomarkets reduces the number of trafficking connections with other countries. We also observed that trafficking on cryptomarkets is more likely to occur between countries that are geographically close. In summary, our study highlights the need to consider both online and offline factors in research on cryptomarkets.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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