Universality and diversity in human song
Bibliographic record
Abstract
What is universal about music across human societies, and what varies? We built a corpus of ethnographic text on musical behavior from a representative sample of the world’s societies and a discography of audio recordings of the music itself. The ethnographic corpus reveals that music appears in every society observed; that variation in musical behavior is well-characterized by three dimensions, which capture the formality, arousal, and religiosity of song events; that musical behavior varies more within societies than across societies on these dimensions; and that music is regularly associated with behavioral contexts such as infant care, healing, dance, and love. The discography, analyzed through four representations (machine summaries, listener ratings, expert annotations, expert transcriptions), revealed that identifiable acoustic features of songs predict their primary behavioral function worldwide, and that these features fall along two dimensions, melodic and rhythmic complexity. These analyses show how applying the tools of computational social science to rich bodies of humanistic data can reveal both universal features and patterns of variability in culture, addressing longstanding debates about each.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".