MuDoc (Multimedia/Music Documentation): a dynamic digital multimedia archive for world music
Bibliographic record
Abstract
In this talk, I'll present a high-level design for MuDoc (Multimedia/Music Documentation), a general-purpose digital repository for ingesting, archiving, and distributing digital multimedia objects, particularly well-suited for multimedia fieldwork in ethnomusicology. MuDoc aims to rectify limitations inherent in traditional ethnomusicological archives, addressing issues such as data preservation, format obsolescence, quality assurance, broad access, dissemination, IPR management, and publication. The goal is to construct a digital multimedia archive database for world music research, education, dissemination, and preservation, which is web-accessible, permanent, searchable, extensible, distributed, and high quality. This project is motivated by broader aims characterizing research in arts, humanities, social science, and technology: to preserve and distribute music and music-related information of aesthetic and cultural value; to support cross-cultural understanding through music scholarship and education; to support diversity of music & music-makers by offering a new venue for production and dissemination outside the music industry; to enable music-related research across the humanities and social sciences; and to enable new music research in computer science, perception, and signal processing.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".