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Record W2186385212 · doi:10.31234/osf.io/s9ryg

CantoCore: A new cross-cultural song classification scheme

2020· article· en· W2186385212 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClassification schemeScheme (mathematics)MusicalProblem of universalsComputer scienceNatural language processingReliability (semiconductor)Cross-culturalArtificial intelligenceLinguisticsMachine learningLiteratureMathematicsArtSociologyAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

Classification of organisms and languages has long provided the foundation for studying biological and cultural history, but there is still no accepted scheme for classifying songs cross-culturally. The best candidate, Lomax and Grauer’s “Cantometrics” coding scheme, did not spawn a large following due, in part, to concerns about its reliability. We present here a new classification scheme, called “CantoCore”, that is inspired by Cantometrics but that emphasizes its “core” structural characters rather than the more subjective characters of performance style. Using both schemes to classify the 30 songs from the Cantometrics Consensus Tape, we found that CantoCore appeared to be approximately 80% more reliable than Cantometrics. Nevertheless, Cantometrics still demonstrated significant reliability for all but its instrumental characters. Future multidisciplinary applications of CantoCore and Cantometrics to the cross-cultural study of musical similarity, musical evolution, musical universals, and the relationship between music and culture will provide the true test of each scheme’s value.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.331
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations77
Published2020
Admission routes1
Has abstractyes

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