Analysis of the Humors in Yue Yunpeng’s Cross Talks: Based on Cooperative Principles
Bibliographic record
Abstract
Similar to the Talk-show in western countries, the cross talk is not only one kind of folk vocal art form but also one kind of performance art form, and the present popularity mostly is because of his humorous use of colloquial language. The Cooperative Principle proposed by the famous American philosopher Grice is an important principle in linguistics. And the violation of the maxims of Cooperative Principle often leads to unexpected humor. Based on Cooperative Principle, this paper analyzes the humor of young comic actor Yue Yunpeng’s cross talks and hopes that people have a better understanding of cross talks and traditional culture. The paper concludes that the violation of the maxim of the quality and relevance mainly produce humor. In the process, when violating the maxims, rhetorical devices are often used. People can find the humorous language of the cross talks directly by data analysis; the audience can better appreciate cross talks’ language humor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".