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Record W2141783528 · doi:10.1177/2158244014547179

Themes of Lust and Love in Popular Music Lyrics From 1971 to 2011

2014· article· en· W2141783528 on OpenAlexaff
Yasaman Madanikia, Kim Bartholomew

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLustLyricsTheme (computing)Human sexualityPopular musicPsychologyRomanceHeteronormativityMusicalLiteratureGender studiesArtSociologyPsychoanalysis

Abstract

fetched live from OpenAlex

We explored themes related to sexual desire (lust) and romantic desire (love) in the lyrics of popular music over the past 40 years. We examined whether there have been changes in the prevalence of lust and love themes and changes in how these themes inter-relate in music lyrics over time. The study sample consisted of the top 40 songs of Billboard Year End Hot 100 single songs for every 5 years from 1971 to 2011 ( N = 360). There was a linear decrease over time in the proportion of songs with a love theme and in the proportion of songs with a combination of lust and love themes. In contrast, there was a significant increase in the proportion of songs with a theme focusing on lust in the absence of love. Themes of lust in the absence of love were especially prevalent in hip-hop/rap music, although music genre did not account for the changing themes over time. These shifts in themes found in popular music may both influence cultural norms and reflect a cultural shift toward acceptance of sexuality outside of love relationships.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.230
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations32
Published2014
Admission routes1
Has abstractyes

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