Understanding Humor Based on the Incongruity Theory and the Cooperative Principle
Bibliographic record
Abstract
Humor plays a crucial role in social interactions; sometimes it is even named as social coping mechanism. People have been working on humor since Plato and Aristotle times and different theories have thus come into being, among which the incongruity theory is considered most influential. This article combines the incongruity theory and a pragmatic principle — the Cooperative Principle (CP) set by H. P. Grice, to explain how humor is generated and perceived in certain context. The analysis shows that people produce humor not just for humor’s sake. Mostly, they want to express an additional message or implicature in Grice’s term. Following Grice’s particularized conversational implicatures generated when conversational maxims of the CP are flouted by participants to convey extra information, the paper terms humor out of exploiting maxims as particularized conversational humor. Detailed analyses of examples of humor have been conducted to elucidate how humor is generated through flouting conversational maxims of the CP and what implicature is put across. Key words : Humor; Cooperative Principle; Incongruity theory
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".