Conflict Management in Illicit Drug Cryptomarkets
Bibliographic record
Abstract
Illegal drug markets have been described as “stateless” systems. Drug dealers, moreover, are commonly considered to have a predilection toward the use of violence to resolve disputes arising from dealing activities. While some studies have undermined this popular perception, new trends surrounding the distribution of illegal drugs via online channels (drug cryptomarkets) have shifted the transactional setting from the physical to virtual realm, thus decreasing the likelihood of violent resolution outcomes even further. This article examines conflict management strategies within cryptomarkets by coding discussion forums between vendors and buyers. Violence, as expected, is absent. Strategies more likely reflect alternatives that have been recognized in conflict management research within and beyond illegal market settings: tolerance, avoidance, ostracism, third-party intervention, negotiation, and threats. The overall setting from which such resolutions emerge is clearly not subject to formal regulations, but our analyses illustrate the multitude of informal social control mechanisms that are consistently at play and which underlie the self-regulatory and communal processes that are firmly in place.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".