Conflict and Victimization in Online Drug Markets
Bibliographic record
Abstract
In the criminal underworld, transactions generate risk for the parties involved, but in contrast to legal markets, parties are unable to turn to legal recourse when cheated in a transaction. Past research has found that many strategies can be used to manage conflicts, including self-help strategies (vengeance, discipline and rebellion, avoidance, negotiation, settlement, and tolerance) and third-party interventions. In the context of illicit drug markets, ostracism and threats or actual violence are also strategies that have been observed. In this paper, we surveyed 49 online illicit drug market vendors to explore the conflict experiences of drug dealers who participate in online and offline illicit drug markets. The paper aims to describe the conflict and victimization experiences of online drug dealers and to understand the mitigating effect of technologies on these conflicts. The results indicate that conflict and victimization experiences are rare for online drug dealers, but there are still many situations that are not mitigated by the use of anonymizing technologies like those used on online illicit markets. We demonstrate how these conflicts differ between online and physical drug markets.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".