Musical Bodies, Musical Minds
Bibliographic record
Abstract
An enactive account of musicality that proposes new ways of thinking about musical experience, musical development in infancy, music and evolution, and more. Musical Bodies, Musical Minds offers an innovative account of human musicality that draws on recent developments in embodied cognitive science. The authors explore musical cognition as a form of sense-making that unfolds across the embodied, environmentally embedded, and sociomaterially extended dimensions that compose the enactment of human worlds of meaning. This perspective enables new ways of understanding musical experience, the development of musicality in infancy and childhood, music's emergence in human evolution, and the nature of musical emotions, empathy, and creativity. Developing their account, the authors link a diverse array of ideas from fields including neuroscience, theoretical biology, psychology, developmental studies, social cognition, and education. Drawing on these insights, they show how dynamic processes of adaptive body-brain-environment interactivity drive musical cognition across a range of contexts, extending it beyond the personal (inner) domain of musical agents and out into the material and social worlds they inhabit and influence. An enactive approach to musicality, they argue, can reveal important aspects of human being and knowing that are often lost or obscured in the modern technologically driven world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".