Grounding the Analysis of Cognitive Processes in Music Performance : Distributed Cognition in Musical Activity
Bibliographic record
Abstract
"Through the systematic analysis of data from music rehearsals, lessons, and performances, this book develops a new conceptual framework for studying cognitive processes in musical activity. Grounding the Analysis of Cognitive Processes in Music Performance draws uniquely on dominant paradigms from the fields of cognitive science, ethnography, anthropology, psychology, and psycholinguistics to develop an ecologically valid framework for the analysis of cognitive processes during musical activity. By presenting close analysis of activities including instrumental performance on the bassoon, lessons on the guitar, and a group rehearsal, chapters provide new insights into the person/instrument system, the musician's use of informational resources, and the organization of perceptual experience during musical performance. Engaging in musical activity is shown to be a highly dynamic and collaborative process invoking tacit knowledge and coordination as musicians identify targets of focal awareness for themselves, their colleagues, and their students. Written by cognitive scientist and classically trained bassoonist, this specialist text builds on two decades of music performance research, and will be of interest to researchers, academics, and postgraduate students in the fields of cognitive psychology and music psychology, as well as musicology, ethnomusicology, music theory, and performance science. Linda T. Kaastra has taught courses in cognitive science, music, and discourse studies at the University of British Columbia and Simon Fraser University. She earned a PhD from UBC's Individual Interdisciplinary Graduate Studies Program"--
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".