Trust Factors in the Social Figuration of Online Drug Trafficking: A Qualitative Content Analysis on a Darknet Market
Bibliographic record
Abstract
The rise in illicit drug trafficking on darknet markets (DNMs) was boosted by those restrictions imposed due to the COVID-19 pandemic. This study aims to put this trend into context by exploring the characteristics of vendors’ services and reputations and understand how products are advertised and what customers tend to value. Qualitative content analysis was conducted on a sample ( n = 100) randomly selected from 6,357 product descriptions and a sample ( n = 500) randomly selected from 34,619 reviews. Both samples are from products found in the drug category of the darknet market Dark0de Reborn. On the supply side, vendors tended to provide basic information on the drugs, a mention of their high quality, the speed and stealth of delivery, their availability for responding to messages, the effects of the drugs, and sometimes even instructions for use. Regarding the demand side, customers usually praised the quality of the product, mentioned the speed and stealth-secure packaging of delivery as essentials, and expressed only a small number of issues. These results support the applicability of Norbert Elias’ social figuration theory in which the interdependencies of the actors are fueled by trust. This theoretical frame sheds light on the social value of the community of DNMs. Furthermore, the findings formulate a robust hypothesis for future research about the previously undervalued role of delivery providers.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".