Modeling – Imaginative Descriptions of Real Things: Learning About Historical Musical Instrument-Making Practices from New Technologies
Bibliographic record
Abstract
Historical musical instrument studies, particularly when framed as organology, have tended to focus on the physical specifics of individual instruments. This article starts from a position in which musical instruments are thought of as a nexus of information: of history of course, of materials certainly, but most of all of ideas. In addition to providing new types of material evidence, digital technologies afford new opportunities for gathering, representing, and interpreting information that might have a considerable impact on our understanding of historical data. Contemporary technologies of modeling and data comparison afford approaches to the interpretation of, for example, the output and goals of a particular workshop, maker, or city that suggest that the study of multiple instruments may be instructive and valuable. Working from a larger data set potentially allows for both greater accuracy and greater subtlety of interpretation. This article will examine both the broad implications of such methodological change and the practical ramifications of learning from modeling multiple instruments.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.011 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".