Bibliographic record
Abstract
This paper uses presupposition theories from semantic, pragmatic and cognitive perspectives to analyze the humor language in traditional Chinese art—xiangsheng (相声), comic dialogue. After a qualitative analysis of three episodes from Da Bao Biao (大保镖), Big Guard, a representative of xiangsheng, the trace of humor in the comic dialogue is detected. I hope this paper can give some hints on how to use presupposition to analyze humor. Key words: xiangsheng (comic dialogue), baofu (cloth wrapper), presupposition humor Resume: Le present article utilise la theorie de presupposition dans les perspectives semantique, pragmatique et cognitive pour analyser le langage humoristique dans l’art traditionnel chinois – xiangsheng, dialogue comique. A travers des analyses quantitatives des trois episodes de Da Bao Biao, Grande Garde, programme representatif de Xiangsheng, la trace de l’humour dans le dialogue comique est detectee. J’espere que cet article peut apporter des eclaircissements a l’utilisation de la presupposition dans l’analyse de l’humour. Mots-Cles: Xiangsheng (dialogue comique), baofu (enveloppe de toile), presupposition, humour 摘要：本文用預設理論分別從語義、語用和認知的角度對傳統中國藝術 -相聲中的幽默進行分析。作者選取了傳統相聲《大保鏢》中的三個片斷，進行了定量分析，由此得出滑稽語言中的幽默來源。作者希望這篇論文能為預設理論分析幽默帶來一些啟發和明示。 關鍵詞：相聲；包袱；幽默；預設
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".