Are Repeat Buyers in Cryptomarkets Loyal Customers? Repeat Business Between Dyads of Cryptomarket Vendors and Users
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
Organizations involved in the sale of illicit products and services have been described as small, ephemeral, and local rather than global. Given their limited size, such organizations are often unable to attract large pools of customers, but it has been noted that organizations that manage to build a small but loyal customer base are likely to be more secure and to incur fewer risks of arrest and victimization. There has been little previous research into the loyalty of repeat buyers on Internet markets but a new technological innovation, cryptomarkets, makes it now more possible to track transactions between vendors and their customers. This article looks at the level of loyalty of cryptomarket repeat buyers by tracking their purchases over time. We find that, on average, customers make 60% of their purchases from the same vendor and that providing increased amounts of information to customers increases the loyalty of cryptomarket vendors’ customer base.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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