https://www.ledgerjournal.org/ojs/ledger/issue/feed 2025-03-04T13:36:20-05:00 Richard Ford Burley ledger.editors@pitt.edu Open Journal Systems <p><em>Ledger</em> is a peer-reviewed scholarly journal that publishes full-length original research articles on the subjects of cryptocurrency and blockchain technology, as well as any relevant intersections with mathematics, computer science, engineering, law, and economics.<em>&nbsp;&nbsp;</em>It is published online by the University Library System, University of Pittsburgh.</p> <p>The journal<em> Ledger</em>:</p> <ul> <li class="show">is open access to all readers,</li> <li class="show">does not charge fees to independent authors or authors with no institutional support,</li> <li class="show">employs a transparent peer-review process,</li> <li class="show">encourages authors to <a href="/ojs/public/journals/1/simplesign.html">digitally sign their manuscripts</a></li> </ul> <p>Authors planning to submit their work to the journal are strongly advised to examine <a href="/ojs/index.php/ledger/about/submissions#authorGuidelines">the Author Guidelines section of the website.</a></p> https://www.ledgerjournal.org/ojs/ledger/article/view/401 2024-12-13T15:51:30-05:00 Lukas Weidener lukas@weidener.eu Bence Lukács bence.lukacs@iabc.dbuas.de

<p>This study addresses the substantial gap in the academic literature on blockchain technology literacy by proposing a Blockchain Technology Literacy Test (BTLT). Through a comprehensive literature review of eight databases, only nine publications with limited focus on blockchain technology literacy were identified. The inconsistency in definitions and predominant emphasis on cryptocurrency literacy further complicate the development of standardized assessments. Based on the results of the literature review and the development of the BTLT, with a focus on terms and technological aspects, an updated list of cryptocurrency literacy questions was proposed as the Cryptocurrency Literacy Test (CLT) to avoid duplications and redundancy in the assessment of relevant blockchain technology and cryptocurrency-related knowledge. The BTLT and CLT aim to distinguish between blockchain technology and cryptocurrency literacy, thereby ensuring comprehensive and accurate assessments. The findings emphasize the need for clear definitions and frameworks within the blockchain ecosystem and call for expanded research to include emerging applications, such as DeFi, DeSci, DAOs, and Web3. This study provides a foundation for future educational efforts and literacy assessments in blockchain technology and cryptocurrencies. </p>

2025-04-29T00:00:00-04:00 Copyright (c) 2025 Lukas Weidener, Bence Lukács https://www.ledgerjournal.org/ojs/ledger/article/view/402 2024-12-13T15:46:01-05:00 Junliang Luo junliang.luo@mail.mcgill.ca Stefan Kitzler kitzler@csh.ac.at Pietro Saggese pietro.saggese@imtlucca.it

<p>We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that, similarly to “financial LEGO bricks”, are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571) and discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.</p>

2025-03-04T00:00:00-05:00 Copyright (c) 2025 Junliang Luo, Stefan Kitzler, Pietro Saggese