Bibliographic record
Abstract
Purpose – This paper aims to explore the challenge posed by Bitcoin to regulators, particularly anti-money laundering regulators. Bitcoin is a crypto-currency based on open-source software and protocols that operates in peer-to-peer networks as a private irreversible payment mechanism. The protocol allows cross-border payments, for large and small items, with little or no transactional costs. Design/methodology/approach – Case studies and case law are examined as are relevant reports by regulators. Findings – Bitcoin is based on complex computer code supported by a robust community in a peer-to-peer network. Unlike other virtual currencies, Bitcoin appears to have obtained purchase and as such poses unique challenges to regulators. Research limitations/implications – Bitcoin is at a nascent stage and the evolution of the virtual currency is difficult to predict. Practical implications – Those who study financial systems, anti-money laundering regimes and asset forfeiture laws will have an interest in this topic. Originality/value – This is a new and emerging currency; there is limited literature on the implications of this currency to anti-money laundering systems.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".