Examining Rhetorical Strategies in Humorous Discourses: A Systematic Review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".