Cryptomarkets and the returns to criminal experience
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
Criminal capital theory suggests more experienced offenders receive higher returns from crime. Offenders who accrue skills over their criminal career are better able to minimize detection, increase profits, and navigate illegal markets. Yet shifts in the offending landscape to technologically-dependent crimes have led some to suggest that the skills necessary to be successful in conventional crimes no longer apply, meaning ‘traditional’ criminals may be left behind. The recent turn of drug vendors to online markets provides an opportunity to investigate whether ‘street smarts’ translate to success in technologically-dependent crimes. This study surveys 51 drug vendors on online drug markets to compare individuals who began their drug-selling career in physical drug markets with vendors whose onset began on digital platforms. The focus is on their criminal earnings while comparing the scope and management of their networks. The results inform potential spillover effects from offline drug-selling into online marketplaces.
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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 | 0.001 | 0.002 |
| 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