Experience and Meaning in Music Performance
Bibliographic record
Abstract
How does the immediate experience of musical sound relate to processes of meaning construction and discursive mediation? \n \nThis question lies at the heart of the studies presented in Experience and Meaning in Music Performance, a unique multi-authored work that both draws on and contributes to current debates in a wide range of disciplines, including ethnomusicology, musicology, psychology, and cognitive science. Addressing a wide range of musical practices from Indian raga and Afro-Brazilian Congado rituals to jazz, rock, and Canadian aboriginal fiddling, the coherence of this study is underpinned by its three main themes: experience, meaning, and performance. Central to all of the studies are moments of performance: those junctures when sound and meaning are actually produced. Experience-what people do, and what they feel, while engaging in music-is equally important. And considered alongside these is meaning: what people put into a performance, what they (and others) get out of it, and, more broadly, how discourses shape performances and experiences of music. In tracing trajectories from moments of musical execution, this volume a novel and productive view of how cultural practice relates to the experience and meaning of musical performance. \n \nA model of interdisciplinary study, and including access to an array of audio-visual materials available on an extensive companion website, Experience and Meaning in Music Performance is essential reading for scholars and students of ethnomusicology and music psychology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".