P<scp>erformance</scp>, G<scp>rouping and</scp>S<scp>chenkerian</scp>A<scp>lternative</scp>R<scp>eadings in</scp>S<scp>ome</scp>P<scp>assages from</scp>B<scp>eethoven</scp>’<scp>s</scp>‘L<scp>ebewohl</scp>’ S<scp>onata</scp>
Bibliographic record
Abstract
ABSTRACT It is proposed that one musically interesting way to characterise and compare different performances or recordings of the same piece is by correlating them with different Schenkerian interpretations through the medium of grouping. This approach is demonstrated through an examination of four ‘either/or’ passages from the first movement of Beethoven's Piano Sonata in E Major, Op. 81a, passages in which at least two Schenkerian interpretations are possible. Schenker's own published and unpublished sketches, among others, are considered alongside recordings by Vladimir Ashkenazy, Emil Gilels, Richard Goode, Murray Perahia and Artur Rubinstein. The approach is not meant to be self‐sufficient, but rather to contribute a new set of tools to the emerging multidisciplinary field of performance studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".