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Record W4224992843 · doi:10.5430/wjel.v12n5p27

Examining Rhetorical Strategies in Humorous Discourses: A Systematic Review

2022· review· en· W4224992843 on OpenAlexvenueno aff
Tianli Zhou, Nor Shahila Mansor, Lay Hoon Ang, Sharon Sharmini

Bibliographic record

VenueWorld Journal of English Language · 2022
Typereview
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsRhetorical questionMetonymyRhetorical deviceIronyMetaphorLinguisticsSelection (genetic algorithm)PsychologySystematic reviewSociologyEpistemologyComputer scienceMEDLINEPhilosophyPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this systematic review is to review the relevant studies on rhetorical strategies in humorous discourse and text to gain a comprehensive understanding of the role of rhetorical strategies in humorous discourse and to explore future research trends. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was conducted in the present study. After data selection and screening followed the protocol criteria, 20 articles were included in this study. The results reveal that most of the prior researchers investigated one specific rhetorical strategy in an article, such as irony, metaphor, satire, insults, and metonymy, while some of them examined different rhetorical strategies simultaneously. Besides, all the included articles adopted a qualitative research method, and the theories they applied are diverse. Moreover, the functions of rhetorical strategies in different humorous discourses are summarized. The results show that the functions of rhetorical strategies differ in different types of humorous contexts, which depend on the speaker’s communication purpose in certain situations.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.058
GPT teacher head0.365
Teacher spread0.306 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2022
Admission routes1
Has abstractyes

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