Ethos Theory of Music: Toward An Empirical Confirmation Through Moral Foundations Theory
Bibliographic record
Abstract
As an advocate of the Ethos Theory of Music, Herbert Spencer argues that sharing in a wide range of musically aroused emotions promotes fellow-feeling thanks to which humans behave considerately toward each other. Here we attempt to provide empirical evidence for this claim. We identified Spencer's fellow-feeling as an instantiation of the concerns for Harm and Fairness Moral Foundations; thus, we predicted that musical expertise, and specifically long-term listening to and playing classical music, would lead to favoring individualizing moral foundations and opposing the binding ones. A cross-national questionnaire (US, Canada, and Italy) was conceived ( N = 330), and the data were analyzed through a parallel mediation Structural Equation Model. Results confirm that musical expertise is associated with lower proclivity toward the binding moral foundations. Conversely, it is connected with an embracement of individualizing moral foundations. Coherently with Spencer's view, such an effect is fully mediated by the emotional way of listening to music.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".