Bibliographic record
Abstract
Blockchain Technology and the Law: Opportunities and Risks is one of the first texts to offer a critical analysis of Blockchain and the legal and economic challenges faced by this new technology. This book will offer those who are unfamiliar with Blockchain an introduction as to how the technology works and will demonstrate how a legal framework that governs it can be used to ensure that it can be successfully deployed. Discussions included in this book: - an introduction to smart contracts, and their potential, from a commercial and consumer law perspective, to change the nature of transactions between parties; - the impact that Blockchain has already had on financial services, and the possible consumer risks and macro-economic issues that may arise in the future; - the challenges that are facing global securities regulators with the development of Initial Coin Offerings and the ongoing risks that they pose to the investing public; - the risk of significant privacy breaches due to the online public nature of Blockchain; and - the future of Blockchain technology. Of interest to academics, policy-makers, technology developers and legal practitioners, this book will provide a thorough examination of Blockchain technology in relation to the law from a comparative perspective with a focus on the United Kingdom, Canada and the United States.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".