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Record W4220937967 · doi:10.5539/ijel.v12n3p1

Metaphor-Based Analysis of Joe Biden’s and George Washington’s Inaugural Speeches

2022· article· en· W4220937967 on OpenAlexvenueno aff
Youness Boussaid

Bibliographic record

VenueInternational Journal of English Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorRhetoricIdeologyPoliticsConceptual metaphorGeorge (robot)SociologyMedia studiesEpistemologyAestheticsSocial sciencePolitical scienceLawArtArt historyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This paper analyzes how conceptual metaphor is used as a persuasive tool in Joseph Biden’s and George Washington’s inaugural speeches. The speeches are analyzed using Conceptual Metaphor Theory. A source-based approach to metaphor analysis is adopted in this paper. Statistical findings are used to examine how metaphor is utilized to frame certain political topics. The study demonstrates that metaphor is a vital persuasive tool in political discourse. The use of conceptual metaphors persuades and appeals to people’s emotions. The paper shows that Biden utilized more conceptual metaphors than Washington. This indicates the need and importance Biden attaches to persuasive rhetoric of which the use of metaphor successfully provides and attains. The nature of conceptual metaphor in both speeches reveals the existence of diachronic differences in how metaphors were used. This metaphor variation which reflects changes in society is ascribed to the differences in ideologies and the zeitgeist of the two eras in which the speeches took place.

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)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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 designNot applicable
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

Citations7
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLanguage, Metaphor, and CognitionFrench-language works237,207