Bibliographic record
Abstract
Understanding the implications and ramifications that accrue at the intersection of cryptocurrency and public policy requires a nuanced definition of cryptocurrency. De-emphasizing the technical definition of cryptocurrency is not only useful for fitting the concept within the realm of public policy but also to be rudimentary in showing how cryptocurrency has implications for some of the very basic principles of governance and democracy. In this chapter, this nuanced definition is pursued. Furthermore, cryptocurrency is not controlled by any major governing body; therefore, its history has been very controversial. The usage of cryptocurrency has grown in the business sphere as consumers are interested in the anonymity that the currency brings. Unfortunately, this anonymity has implications for criminality and criminal use. This and other factors such as the inability to tax the currency have led to governments globally having mixed reactions to cryptocurrency. The evolution of cryptocurrency has received differential attention and action from governments around the road. Arguably, governments’ action and inaction have both shaped the evolution of cryptocurrency. Countries such as the United States and Canada have extensive laws and regulations on cryptocurrency use which allow citizens to utilize the currency freely and openly. Other countries such as China and Russia have discouraged their citizens with a range of restrictions that inhibit their usage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.034 |
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".