Bibliographic record
Abstract
"The issuance of stablecoins and other cryptocurrencies continues to increase, highlighting the importance of evaluating the risks these innovations pose to the financial system. However, given the novelty of stablecoin arrangements, questions remain about how authorities should analyze them and what they should be concerned about: How should we classify the different parts of the stablecoin arrangement? How can we quantify the risks in a way that is comparable to other risks to the financial system? And how can we ensure risks are evaluated consistently across stablecoin arrangements? To address these questions, we propose a three-step framework that authorities can use to assess the risks of a stablecoin arrangement: classifying the stablecoin arrangement into three parts—coin structure, related transfer system(s) and related financial service(s)—and categorizing the attributes of each part identifying specific risk scenarios that are relevant to the stablecoin arrangement quantifying the range of probable loss and possible frequency associated with the identified risk scenarios Our proposed framework allows authorities to understand the defining characteristics of stablecoin arrangements, to be specific about any concerns they may have, and to be objective in their treatment from issuer to issuer."
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".