Review of John Paul Ito, <i>Focal Impulse Theory: Musical Expression, Meter, and the Body</i> (Indiana University Press, 2020)
Bibliographic record
Abstract
Example 1 presents the beginning of the Loure from Johann Sebastian Bach's Partita No. 3 for Solo Violin.With this movement, there seem to be two main performance traditions.Jascha Heifetz (1952, Audio Example 1) represents one approach, along with Itzhak Perlman (1988), Hilary Hahn (1997), and Midori (2015).Sergiu Luca (1977, Audio Example 2) represents another, along with Suyoen Kim (2011) andGil Shaham (2015).How can we compare these interpretations?We might start from individual musical elements: Heifetz's articulation is more legato, Luca's tempo is slightly faster, and so forth.Alternatively, we might consider the overall character of these performances.As I hear it, Heifetz's is more stately, while Luca's is more dance-like, in keeping with descriptions of the loure as a slow gigue (Little 2001).Similarly, one YouTube viewer appreciates Hahn's interpretation for its "tenderness," though Shaham's is "maybe more FUN" (simiamens n.d.).But how can we connect the performances' musical details to these expressive qualities?John Paul Ito's new book offers a principled way to integrate these levels, based on performers' bodily movement.For Ito, these interpretive traditions differ in their placement of focal impulses. 2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".