Lessons learned in a large-scale project to digitize and computationally analyze musical scores
Bibliographic record
Abstract
Abstract Many areas of the digital humanities (DH) have the potential to benefit greatly from recent advances in machine learning, big data, and statistical analysis. These sophisticated techniques come with pitfalls, however, and their accidental misuse can lead to erroneous results. This article outlines in broad terms our experiences with a large-scale, long-term international project to digitize musical scores, automatically analyze them, and share the results with other researchers. It then describes our experiences in order to help other researchers in the DH avoid some of the missteps we and other DH researchers have made. In addition to issues associated with data mining, this article also discusses approaches to sharing data, software, and intermediate analyses such that they are accessible to other researchers in ways that encourage repeatability, verifiability, iterative refinement, creative exploration, and multidisciplinary collaboration.
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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 | 0.096 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".