Bibliographic record
Abstract
We were very happy to be the guest editors for this issue and excited to have the opportunity to work with scholars and professionals from around the world in developing this examination of digital projects. This special issue of Fontes Artis Musicae focuses on digital music projects and it covers a wide variety of topics and displays diverse aspects of the digital world that librarians and scholars may be involved in or come in contact with. When organizing the articles, we have purposely put them in an order that simulates the processes of a digital/digitization project, starting from the coming and the creating of a formal music online presence on the Internet, the setting up of a web-based music documentation database, and culminating in a reflection on how the Internet has changed the way scholars conduct research. We have also attempted to showcase projects from a wide range of countries so that a good part of the world is represented here. In other words, this is truly an international issue that tributes to the greatness of the digital world! The first article, and by Bob Kosovsky from the United States of America, discusses the history of the Opera-L email distribution list, the background of the current opera entries in Wikipedia, and how Opera-L looked at the discussion and complaints about the inaccurate and rather non-professional contents submitted by Internet users on Wikipedia and decided to change things. Thanks to the joint effort of the Opera-L community, over 8,600 opera entries have been greatly improved, enhanced, and corrected to the benefit of all Wikipedia users of this collaboratively-written free Internet encyclopedia. The second article, Challenges to Music Documentation, by Hyun Kyung Chae and three colleagues from South Korea, describes the challenges they encountered when they began working as part of the international project Repertoire International des Sources Musicale (RISM) and registering music data into RISM's Kallisto software. In the article, they elaborate on the detailed setup of their first music education database for East Asian music that now serves as the pioneer project for what has become a unified primary source repository of modern music of East Asia. An innovation of the repository is that it can handle multiple East Asian languages. For developers who are looking to create a searchable database, they can find both practical tips and useful advice used by the team. In the third article, Zong Woo Geem and Jeong-Yoon Choi, also from South Korea, cover the Potential of Music-Inspired Algorithm for Music Document Grouping. This paper is more technical in nature. By going beyond the traditional semantic web, their proposed Harmony Search Algorithm, which numerically simulates jazz musicians' improvisation processes, is suggested as being useful in classifying music sound files or clustering music-related documents with higher efficiency and relevancy. When talking about digitization projects, The International Music Score Library Project (IMSLP) from Canada is a resource upon which many of us rely heavily. Prompted by the recent heated discussion on various listservs, Edward Guo, the creator and leader of IMSLP, gives us A Librarian's Guide to The paper gives us an in-depth overview on how and why this ground-breaking project was born, the philosophy that the creators have behind this project, and some highlights on the special classification and metadata employed that make IMSLP a truly useful adjunct to more traditional paper-based music score holdings. We think this article will make you look at IMSLP and its potential in a different light. In Europe, Jurgen Diet of Germany covers Digitization and Presentation of Music Documents in the Bavarian State Library. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.156 | 0.051 |
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".