Regulation of the Crypto-Economy: Managing Risks, Challenges, and Regulatory Uncertainty
Bibliographic record
Abstract
Distributed ledger technology, also known as the blockchain, is gaining traction globally. Blockchain offers a secure validation mechanism and decentralized mass collaboration. Cryptocurrencies make use of this technology as a new asset class for investors worldwide. Cryptocurrencies are being used by companies to raise capital via initial coin offerings (ICOs). The substantial inflow of unregulated capital into a transactional and transnational industry has aroused interest from not just investors, but also national securities and monetary regulatory agencies. In this paper, we review the Security and Exchange Commission’s initial statements and subsequent pronouncements on ICO’s to illustrate the potential problems with applying an older legal framework to an ever-evolving ecosystem. Recognizing the inability of enforcement within existing regulatory frameworks, we discuss the importance of regulation of the crypto asset class and internal collaboration between government agencies and developers in the establishment of an ecosystem that integrates investor protection and investments.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".