Conflict and Victimization in Online Drug Markets
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
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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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 | 0.000 | 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