Examining the Uncharted Dark Web: Trust Signalling on Single Vendor Shops
Bibliographic record
Abstract
Despite their growing popularity, cryptomarkets generate risks for participants. This has promoted the reemergence of a more personal transaction model on the dark web: single vendor shops. To date, little is known about how single vendors display trust to attract potential customers without relying on the structural trust provided by cryptomarkets’ review and escrow systems. A total of 108 single vendor shops were identified. A coding grid was used to determine whether vendors displayed any of the four categories of trust signals typically found on cryptomarkets (i.e., signals related to identity, marketing, security, and signals that directly express trust). While the majority of single vendor shops were involved in illicit drug dealing, other products such as electronics, weapons, and fake documents were also offered. On average, shops displayed few trust signals. However, variations between different kinds of vendors were found: while vendors involved in illicit drug dealing displayed more identity- and marketing-related trust signals, vendors involved in fraud displayed more security-related signals and signals that directly expressed trust. Differences between vendors might be due to the nature of the products they offer and to the level of competition in their respective markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".