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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".