A Multi- Pragmatic Study of Sarcasm in Political Texts
Bibliographic record
Abstract
Sarcasm is one of the strategies that people use to attack the listener indirectly or in a way that seems to be kind on the surface. There is a shady relation between sarcasm and irony. Accordingly, this study sets the following aims which are identifying the most frequent type of speech acts that is used to convey the sarcastic meaning; knowing the conversational maxim that is breached by the sarcast to express sarcasm; shading light on the type of politeness maxim that is violated in the sarcastic messages; specifying the social functions of sarcasm in political texts; and revealing the linguistic mechanisms that are employed to reflect sarcasm. The researcher hypothesizes the following: expressives are the most frequent type of speech acts which are used to reflect sarcasm; sarcasm is the result of breaching quality maxim only; the most common violated politeness maxim in sarcasm is tact maxim; sarcasm mainly serves as a social control tool in the political contexts; and metaphor and explicitation are the most frequent mechanisms of sarcasm in political texts. The researcher adopts an eclectic model which consists of Speech Acts Theory of Searle and Vanderveken (1985), Grice's Conversational Implicature (1989), Leech's Politeness Principle (1983-2014), Ducharme's Functions of Sarcasm (1994), and Tabacaru's Linguistic Mechanisms of Sarcasm (2019). By using this model, the researcher finds that the most frequent type of speech acts is assertives, breaching quality maxim is a basic requirement to reflect sarcasm, the approbation maxim is the most violated maxim in the sarcastic texts, the dominant function of sarcasm in political texts is social control, and metaphor is used more frequent in the political context than other mechanisms.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".