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Record W3122557489 · doi:10.3968/11967

A Cognitive Analysis of Conceptual Metaphor in the Lyrics of Taylor Swift

2020· article· en· W3122557489 on OpenAlexvenueno aff
Chuyi Liao

Bibliographic record

VenueCross-cultural communication · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorLyricsConceptual metaphorMeaning (existential)SwiftLinguisticsLiteral and figurative languageEpistemologyPsychologyLiteratureComputer scienceArtPhilosophy

Abstract

fetched live from OpenAlex

Where there is a language, there is a metaphor. Metaphor is a kind of cognition phenomenon that can reflect and influence human beings’ mindset, so does lyrics. The present study aims to explore the general distribution and practical meaning of conceptual metaphor theory in the identified conceptual metaphors in the lyrics written by Taylor Swift. Based on a corpus of all the lyrics from the selected twenty Taylor Swift’s most popular songs, the identified and categorized conceptual metaphors in the data are analyzed by discourse analysis in accordance to Lakoff and Johnson’s Conceptual Metaphor Theory. Upon investigation, the general distribution of conceptual metaphors in the selected lyrics written by Taylor Swift is mainly divided into two categories, structural metaphor and ontological metaphor. The result of the discourse analysis implies that conceptual metaphor plays a key role in the lyrics written by Taylor Swift.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.382
Teacher spread0.316 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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