The Expanded Natural History of Song Discography, A Global Corpus of Vocal Music
Bibliographic record
Abstract
Abstract A comprehensive cognitive science requires broad sampling of human behavior to justify general inferences about the mind. For example, the field of psycholinguistics relies on a rich history of comparative study, with many available resources that systematically document many languages. Surprisingly, despite a longstanding interest in questions of universality and diversity, the psychology of music has few such resources. Here, we report the Expanded Natural History of Song Discography, an open-access corpus of vocal music (n = 1007 song excerpts), with accompanying metadata detailing each song’s region of origin, language (of 413 languages represented here), and one of 10 behavioral contexts (e.g., work, storytelling, mourning, lullaby, dance). The corpus is designed to sample both broadly, with a large cross-section of societies and languages; and deeply, with many songs representing three well-studied language families (Atlantic-Congo, Austronesian, and Indo-European). This design facilitates direct comparison of musical and vocal features across cultures, principled approaches to sampling stimuli for experiments, and evaluation of models of the cultural evolution of song. In this paper we describe the corpus and provide two proofs of concept, demonstrating its utility. We report (1) a conceptual replication of previous findings that the acoustical forms of songs are predictive of their behavioral contexts, including in previously unstudied contexts (e.g., children’s play songs); and (2) similarities in acoustic content of songs across cultures are predictable, in part, by the relatedness of those cultures.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".