Introduction to the Special Issue on Mathematical Research for Blockchain Economy
Bibliographic record
Abstract
Introduction to the Special Issue on Mathematical Research for Blockchain EconomyBlockchain Technology has been considered as the most revolutionizing invention since the Internet.Because of its immutable nature and the associated security and privacy benefits, it has widely attracted the attention of banks, governments, techno-corporations and venture investors.Blockchain applications range from finance to healthcare, from education and media to logistics, NFTs and many more.However, the theoretical limitations and technical barriers to the adoption of blockchain such as scalability, latency, privacy and security need to be further studied and addressed in high-quality research.This special issue of the ACM Distributed Ledger Technologies: Research and Practice (ACM DLT) journals contains selected and refereed papers on the topic of Mathematical Research in Blockchain Economies.Preliminary versions of some of the papers appeared in the 2022 edition of the International Conference on Mathematical Research for Blockchain Economy (MARBLE'22), which took place in Vilamoura, Portugal, from July 12 to 24, 2022.Following the paradigm of the conference, the current special issue provides a high-profile, cutting-edge platform for mathematicians, computer scientists and economists, from both industry and practice, to present the latest advances and innovations in key theories of blockchain.Having a broad international appeal, both the MARBLE conference and the current special issue focuses on the mathematics behind blockchain to bridge the gap between theory and practice.The three selected article in this special issue were selected from 10 submitted manuscripts, following the standard, rigorous ACM DLT review procedures.The articles cover topics in decentralized finance, smart contracts and game-theoretic modelling of blockchains.The content of the articles is as follows.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".