How to Design Cryptocurrency Value and How to Secure Its Sustainability in the Market
Bibliographic record
Abstract
The purpose of this study is to analyze the contents of cryptocurrency value design based on adaptability to the current market. It is also intended to provide a method of issuing cryptocurrency before its creation, and an operation method afterwards. Activities before the creation of cryptocurrency must determine desirable behaviors and rewards to create value, and suggest countermeasures to prevent participants from engaging in undesirable behaviors. After the creation of a cryptocurrency, it is necessary to propose a method to induce scarcity and increase demand so that the value of the generated cryptocurrency can be sustained. To observe this, we looked at the contents of the value design of the eight types of cryptocurrencies currently in use in the market. Some cryptocurrencies, such as Bitcoin, are choosing mining as a reward, to secure scarcity for maintaining the value of cryptocurrency, limiting the amount of issuance, and burning the already issued cryptocurrency in the market. Also, increasing demand helps maintain the value of cryptocurrency. This study can contribute to supporting the growth of a healthy cryptocurrency market through cryptocurrency-related research.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".