Bibliographic record
Abstract
Reporting on work carried out in conjunction with Andrew Earis and Craig Sapp, this paper introduces recently developed approaches to the analysis of recorded music, illustrating them in terms of selected Chopin mazurkas. Topics covered include the stylistic characterisation and aesthetic values of Paderewski's playing of Op. 17 No. 4, contrasted with performances from the last quarter of the twentieth century, as well as relationships between different pianists' interpretations of Op. 68 No. 3. A possible performance genealogy of performances of the latter is proposed, in which recordings by Rubinstein and Cortot play a key role, while clustering based on Pearson correlation of tempo data yields relationships supported in one instance by documented teacher/pupil relationships. Representing the early outcomes of a more extended research project, these findings are encouraging in that it appears possible to draw meaningful conclusions from the consideration only of tempo data. The current phase of the project is also working with rhythmic and dynamic data, which should significantly enhance the potential for objective modelling of musically meaningful relationships.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".