What About Their Performance Do Free Jazz Improvisers Agree Upon? A Case Study
Bibliographic record
Abstract
When musicians improvise freely together-not following any sort of script, predetermined harmonic structure, or "referent"-to what extent do they understand what they are doing in the same way as each other? And to what extent is their understanding privileged relative to outside listeners with similar levels of performing experience in free improvisation? In this exploratory case study, a saxophonist and a pianist of international renown who knew each other's work but who had never performed together before were recorded while improvising freely for 40 min. Immediately afterwards the performers were interviewed separately about the just-completed improvisation, first from memory and then while listening to two 5 min excerpts of the recording in order to prompt specific and detailed commentary. Two commenting listeners from the same performance community (a saxophonist and drummer) listened to, and were interviewed about, these excerpts. Some months later, all four participants rated the extent to which they endorsed 302 statements that had been extracted from the four interviews and anonymized. The findings demonstrate that these free jazz improvisers characterized the improvisation quite differently, selecting different moments to comment about and with little overlap in the content of their characterizations. The performers were not more likely to endorse statements by their performing partner than by a commenting listener from the same performance community, and their patterns of agreement with each other (endorsing or dissenting with statements) across multiple ratings-their interrater reliability as measured with Cohen's kappa-was only moderate, and not consistently higher than their agreement with the commenting listeners. These performers were more likely to endorse statements about performers' thoughts and actions than statements about the music itself, and more likely to endorse evaluatively positive than negative statements. But these kinds of statements were polarizing; the performers were more likely to agree with each other in their ratings of statements about the music itself and negative statements. As in Schober and Spiro (2014), the evidence supports a view that fully shared understanding is not needed for joint improvisation by professional musicians in this genre and that performing partners can agree with an outside listener more than with each other.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 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".