Bibliographic record
Abstract
The LinkedMusic Project is a 7-year initiative to transform music research by constructing a global digital music library that integrates diverse music data sources into a unified system. Despite advances in text search technologies, music searches remain constrained by disparate formats and incompatible metadata schemas. By applying linked data principles, we are developing new tools that will enable seamless access across music database platforms. Central to the project is the integration of the Resource Description Framework (RDF) for standardized data representation with Natural Language Query to SPARQL (NLQ2SPARQL) for intuitive search capabilities. By transforming heterogeneous music databases into RDF and leveraging natural language processing capabilities of large language models (LLMs), the project creates a robust, scalable framework that ensures data integrity while enhancing accessibility. To facilitate user access to this vast and interconnected digital library, we are developing SESEMMI, an open-source metasearch engine that enables simultaneous searches across multiple databases without requiring any modifications to their underlying schemas. A distinctive feature of the LinkedMusic Project is our commitment to multilingual and culturally sensitive search capabilities, empowering users to explore global music genres, traditions, and performers through culturally sensitive terms and queries in multiple languages. Our goal is to enhance access for scholars and the public, fostering research and creative output.
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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.035 |
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".