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Record W4246876420 · doi:10.31219/osf.io/nmruj

People Prefer Simpler Content When There Are More Choices: A Time Series Analysis of Lyrical Complexity in Six Decades of American Popular Music

2019· preprint· en· W4246876420 on OpenAlexaff
Michael E. W. Varnum, Jaimie Arona Krems, Colin Morris, Igor Grossmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLyricsSimplicityPopular musicVariety (cybernetics)Meaning (existential)Cultural transmission in animalsPopularityAutocorrelationContent (measure theory)Test (biology)Affect (linguistics)LiteratureHistoryPsychologyComputer scienceSocial psychologyArtMathematicsCommunicationStatisticsEpistemologyArtificial intelligencePhilosophyEcology

Abstract

fetched live from OpenAlex

Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has been used as an index of shifting norms, affect, and values at the cultural level. One remarkable, recently-uncovered trend is that successful pop songs have increasingly simple lyrics. Why? We test the idea that increasing lyrical simplicity is linked to a widening array of novel song choices. To test this Cultural Compression Hypothesis (CCH), we examined six decades of popular music (N = 14,661 songs). The number of novel song choices predicted greater lyrical simplicity of successful songs. This relationship was robust, holding when controlling for critical ecological and demographic factors and also when using a variety of approaches to account for the potentially confounding influence of temporal autocorrelation. The present data provide the first time series evidence that real-world cultural transmission may depend on the amount of novel choices in the information landscape.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.333
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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