Can Crypto Currencies Challenge Sovereign Currencies? A Multidisciplinary Overview of Opportunities and Risks
Bibliographic record
Abstract
Considered as a niche phenomenon, a kind of technological folklore, which could disappear overnight, cryptocurrency has been the subject of few multidisciplinary analyses to understand how a series of numbers, supported by no power to impose its use, could constitute a currency? The review of the available literature reveals a state of knowledge scattered in the different disciplines that are interested in it. The objective of this article is to remedy this by aggregating essential historical, economic, legal and technological knowledge developed in the study and analysis of this technical-financial innovation. The aim is to examine the opportunities, challenges and risks of using cryptocurrencies as an alternative to sovereign currency, through a nuance between the optimism of those who see in cryptocurrencies liberation from the monetary constraints of States, and the hostility of those who see in these innovations a utopian monetary system or a lever of incitement to crime. A concluding discussion will expose the trend and some recommendations for supporting eventual implementation with the least criminogenic effect.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".