Bibliographic record
Abstract
The technologies that have been used to create new kinds of payment systems — cryptography and mobile phones, biometrics and blockchains — can also be used to create new kinds of money. While commercial banks could use these new technologies to manage wholly digital versions of existing fiat currencies, the low cost and widespread availability of those technologies mean that organisations other than nation states can also think about creating digital currencies. This paper builds on a previous paper that explored who these organisations might be (the ‘5Cs framework’) and investigated their motivations, to look at two specific and contrasting proposals that move these discussions from theoretical to actual policy concerns. These examples are taken from the private sector (Facebook’s Libra) and the public sector (the People’s Bank of China digital currency). The paper argues that the competition between these digital currencies is about hegemony not hash rates, and that shifts in the tectonic plates of economic power ultimately result in earthquakes that change the landscape of political power.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".