Bibliographic record
Abstract
Abstract Listening to music entails processes in which auditory input is automatically analyzed and classified, and conscious processes in which listeners interpret and evaluate the music. Performing music involves engaging in rehearsed movements that reflect procedural (embodied) knowledge of music, along with conscious efforts to guide and refine these movements through online monitoring of the sounded output. Composing music balances the use of intuition that reflects implicit knowledge of music with conscious and deliberate efforts to invent musical textures and devices that are innovative and consistent with individual aesthetic goals. Listeners and musicians also interact with one another in ways that blur the boundary between them: Listeners tap or clap in time with music, monitor the facial expressions and gestures of performers, and empathize emotionally with musicians; musicians, in turn, attend to their audience and perform differently depending on the perceived energy and attitude of their listeners. Musicians and listeners are roped together through shared cognitive, emotional, and motor experiences, exhibiting remarkable synchrony in behavior and thought. In this chapter, we describe the forms of musical thought for musicians and listeners, and we discuss the implications of implicit and explicit thought processes for musical understanding and emotional experience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".