What Does Musicology Have to Do With Archiving? Three Experiences of Engagement
Bibliographic record
Abstract
Musical practices derived from post-1960s experimental music created heterogeneous musical materials and traces—including scores, preparations and instrument modifications, electronic instruments, custom-made devices, and recordings. The Romantic work concept on which most traditional musical archives are based is unsuitable to preserve this expanded apparatus of objects and concepts, and rethinking the musical archive is becoming urgent. This colloquy collected the experiences of three researchers, engaging with five institutions, three creators, and four countries. Yet the archival issues presented are eerily similar. These experiences involve David Tudor (paper-based archive at the Getty Research Institute, Los Angeles, CA , and the David Tudor Instrument Collection at Wesleyan University, Midtown, CT ); Mario Bertoncini (paper-based archive at the archive of the Akademie der Künste, Berlin, and his object collection at the moment stored at the Fondazione Isabella Scelsi, Rome); Gayle Young (who still owns all her production).
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.052 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".