The Song Remains the Same: A Replication and Extension of the MUSIC Model
Bibliographic record
Abstract
There is overwhelming anecdotal and empirical evidence for individual differences in musical preferences. However, little is known about what drives those preferences. Are people drawn to particular musical genres (e.g., rap, jazz) or to certain musical properties (e.g., lively, loud)? Recent findings suggest that musical preferences can be conceptualized in terms of five orthogonal dimensions: Mellow, Unpretentious, Sophisticated, Intense, and Contemporary (conveniently, MUSIC). The aim of the present research is to replicate and extend that work by empirically examining the hypothesis that musical preferences are based on preferences for particular musical properties and psychological attributes as opposed to musical genres. Findings from Study 1 replicated the five-factor MUSIC structure using musical excerpts from a variety of genres and subgenres and revealed musical attributes that differentiate each factor. Results from Studies 2 and 3 show that the MUSIC structure is recoverable using musical pieces from only the jazz and rock genres, respectively. Taken together, the current work provides strong evidence that preferences for music are determined by specific musical attributes and that the MUSIC model is a robust framework for conceptualizing and measuring such preferences.
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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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".