Cross-Cultural Similarities and Differences
Bibliographic record
Abstract
Abstract This chapter reviews empirical studies of music and emotion that involve a cross-cultural comparison, and outlines prevailing views on the implications of such studies. It begins by discussing some theoretical implications of research on cross-cultural commonalities in the association between music and emotion. Section 27.2 reviews the central questions arising from cross-cultural research on emotion. Section 27.3 outlines the cue-redundancy model, developed to account for cross-cultural similarities and differences in the expression and recognition of emotion in music. Section 27.4 presents a broader framework for summarizing existing data on emotional communication, referred to as fractionating emotional systems (FES). FES extend the cue-redundancy model by accounting for similarities and differences in emotional communication, not only across cultures but also across the auditory channels of music and speech prosody. Section 27.5 reviews cross-cultural studies of music and emotion, while section 27.6 reviews cross-cultural studies of emotion in speech. Section 27.7 identifies some future prospects for the cross-cultural study of music and emotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".