The Evolution of Currency: Cash to Cryptos to Sovereign Digital Currencies
Bibliographic record
Abstract
In 2009, Bitcoin created a world-first decentralised alternative currency that has spawned over 1,700 imitations by private parties. In 2018, governments finally joined the race, as Venezuela issued a world-first sovereign digital currency. Major economies like Canada, China, Singapore and the UK are all developing their own versions. These new versions differ significantly from Bitcoin and among themselves, creating the potential to flood the global financial system with a myriad of new digital currencies. Existing taxonomies of currency struggle with the speed of change (frequently due to inadequate understanding of the underlying technology) and, as a result, remain incomplete and filled with confusing and conflicting vocabulary (with terms like ‘virtual currencies’, ‘digital currencies’, ‘cryptocurrencies’ frequently being used to refer to the same thing). This article resolves this problem. First, it analyses existing forms of currency based on their functional characteristics and provides a comprehensive taxonomy. Second, it integrates the likely forms of upcoming sovereign digital currencies into this taxonomy and outlines the corresponding challenges. At the moment, no major economy seems keen to issue a sovereign digital currency, but if one does, others will, for good reasons, respond in kind and the ground will be laid for a sovereign digital currency battle royale.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".