Why are song lyrics becoming simpler? a time series analysis of lyrical complexity in six decades of American popular music
Bibliographic record
Abstract
Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has been used as an index of culture's shifting norms, affect, and values. One particular, newly uncovered, trend is that lyrics of popular songs have become increasingly simple over time. Why might this be? Here, we test the idea that increasing lyrical simplicity is accompanied by a widening array of novel song choices. We do so by using six decades (1958-2016) of popular music in the United States (N = 14,661 songs), controlling for multiple well-studied ecological and cultural factors plausibly linked to shifts in lyrical simplicity (e.g., resource availability, pathogen prevalence, rising individualism). In years when more novel song choices were produced, the average lyrical simplicity of the songs entering U.S. billboard charts was greater. This cross-temporal relationship was robust when controlling for a range of cultural and ecological factors and employing multiverse analyses to control for potentially confounding influence of temporal autocorrelation. Finally, simpler songs entering the charts were more successful, reaching higher chart positions, especially in years when more novel songs were produced. The present results suggest that cultural transmission depends on the amount of novel choices in the information landscape.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".