Deepening Musical Performance through Movement: The Theory and Practice of Embodied Interpretation. By Alexandra Pierce. * Sound in Motion: A Performer's Guide to Greater Musical Expression. By David McGill.
Bibliographic record
Abstract
John Blacking used to tell the story of his search, as a young man, for a more expressive way of playing a Beethoven sonata. After a long while, he discovered that the secret was to move the stool an inch nearer the keyboard. Is musical expression physical or mental? These two books take opposite sides in this discussion. ‘Musical thinking solves technical problems’, writes David McGill (p. 264); ‘logical thinking about musical phrasing mentally unites the elements of technique and musicality’ (p. 267). Alexandra Pierce takes a different view, proclaiming that ‘movement to music provides a spiral for ongoing transformation, for becoming more fluent, coherent, and shapely in expression’ (p. 1). ‘This active, inner engagement’, she continues, ‘has quite a different appearance from that of someone who is giving thought to a topic or who knows the answers already’ (p. 7). McGill and Pierce come from different worlds. He is...
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".