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Record W2573505280 · doi:10.5539/ijel.v7n2p52

Conceptual Metaphors and Rhetoric in Barack Obama’s and Xi Jinping’s Diplomatic Discourse in Africa and Europe

2017· article· en· W2573505280 on OpenAlexvenueno aff
Irungu Wageche

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorRhetoricRhetorical questionConceptual metaphorSociologyPacePolitical scienceEpistemologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This paper examines the use of conceptual metaphors in Barack Obama’s and Xi Jinping’s diplomatic discourse in both Africa and Europe. Drawing on four speeches, this paper begins by examining the pervasiveness of metaphor utility in the speeches by using Pragglejazz Metaphor Identification Procedure. This paper examines the underlying concepts in the identified metaphors by using Lakoff and Johnson conceptual metaphor framework. Finally, this paper examines the significance of conceptual metaphors as a rhetorical strategy in diplomatic discourse. This paper found out that both Barack Obama and Xi Jinping employed an exceptionally high number of metaphors in their discourse in Africa and Europe. We found out that metaphors used by each leader do form an underlying concept. Barack Obama’s diplomatic discourse embodies journey metaphors while Xi Jinping’s diplomatic discourse embodies nature metaphors. The paper illustrates how both leaders draw on neutral lexical units such as distance, crossroads, pace, path, water, lions, mountains, wells, et cetera and charge them with metaphors as a rhetorical strategy in order to draw African and European audiences closer to their primary message.

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)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.332
Teacher spread0.300 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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