Improvisation, Correlation, and Vibration: An Interview with Steve Coleman
Bibliographic record
Abstract
While working on a study on the relation between improvisation and language (Depth and Indebtedness: Jazz, Improvisation, Language, forthcoming 2007), I asked saxophonist Steve Coleman to participate in a Berlin-New York phone interview. Although I was well aware of how outspoken and eloquent an artist Coleman is (an amateur saxophonist myself, I had taken Steve's improvisation class at U.C. Berkeley, where he taught from 2000 to 2002), I was taken by surprise by Steve's endurance. After two hours, I ran out of fresh tape. Luckily I found some old ones to overdub, as Steve kept unfolding his philosophy of cosmic energy, as well as his ideas on improvisation, language, structure, freedom and innovation, often making his points with the help of highly personal anecdotes. Still going strong after four hours non-stop, Steve explained his motivation: "If we're going to do this, we better do it right." JV
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".