Digital assets and the potential for global systemic risk
Bibliographic record
Abstract
The volatile value of digital assets, especially crypto-assets, makes them risky options for investors. This inherent risk limits both their market size and the purposes for which they are used, thus reducing the possibility of systemic risk arising. But digital assets and the mainstream financial system are becoming increasingly interconnected, important and complex. Stablecoins, for example, are used more widely for payments because they are perceived as more dependable, but their link to fiat currency creates the risk of panicked investors cashing them out, similar to bank runs. The relative safety of central bank digital currency (CBDC) could make it too attractive, causing rapid shifts of deposits into CBDC in times of stress, resulting in faster and larger bank runs. Distributed ledger technology could simplify trading relationships, thus eliminating many margins and fees, but while this approach promotes efficiency, it could also weaken financial institutions that rely on revenue from these sources. The author recommends that regulators be aware of these risks and take precautions to mitigate them.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".