The Crime-Crypto Nexus: Nuancing Risk Across Crypto-Crime Transactions
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
Abstract Cryptocurrency is supercharging illicit activities by transnational criminal networks, including terrorism, drug trafficking, pornography, sanctions evasion, and ransomware. Yet, mainstream cryptocurrency literature often overlooks this criminal association. The relatively new and transboundary nature of cryptocurrency is restructuring criminal activities. Hacking has emerged as a digital-age bank heist, siphoning off substantial sums from exchange platforms. Crypto crime is dynamic, transitioning from primarily placing and layering the proceeds of precursor crimes into the financial system to a burgeoning trend of stealing virtual currency. While not every online financial crime involves cryptocurrency, the proliferation of crypto-enabled cybercrimes is exponential. Paradoxically, existing literature largely disregards how cryptocurrency-enabled offenses such as Online Child Sexual Exploitation and Abuse (OCSEA), sanctions evasion, and ransomware.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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