Cross-cultural music corpus: The Expanded Natural History of Song Discography
Bibliographic record
Abstract
This repository hosts the Expanded Natural History of Song Discography. It contains 1007 audio recordings of vocal music gathered from many human societies, each annotated with a world region, language, and behavioural context. Each song file contains a 10-second excerpt of the source audio, selected at random from only portions of the recording that contain an audible singer. Given the short form of each excerpt, and the intended use of these files only for research purposes, they have been made available under Fair Use. NHS2-songs.zip contains the audio files, volume-matched and with 1s fade in/out added, in MP3 format. These can be analysed as-is or used in experiments. NHS2-metadata.csv contains annotations, where each row corresponds to a song. The four columns include song, which includes a unique identifier for each song in the format `NHS2-XXXX.mp3`; region, which indicates an approximate geographical location where the song was recorded, using Human Relations Area Files categories (see https://ehrafworldcultures.yale.edu); glottocode, which indicates the language in which the song is produced (see https://glottolog.org); and type, which indicates the behavioural context in which the song was produced, from a set of 10 categories (dance, healing, love, lullaby, play, procession, mourning, work, story, and praise). For assistance with the corpus, contact Martynas Snarskis (martysnarskis@gmail.com), Mila Bertolo (mila.bertolo@mail.mcgill.ca), and Samuel Mehr (mehr@hey.com). Further information about the construction of this corpus will be made available in a forthcoming paper; we will update this Zenodo archive when the paper is publicly available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".