Bibliographic record
Abstract
English jokes improve human relations and enliven the communicative atmosphere in daily communication. How to generate English jokes has long been of interest to numerous researchers, such as philosophers, psychologists and linguists. In the linguistic field, the scholars at home and abroad have been discussing English jokes from angles of rhetoric, phonetics, semantics, pragmatics and cognitive linguistics. Few researchers study the generation of English jokes from cognitive linguistics. Therefore, this study implements a qualitative cognitive linguistic exploration into English jokes and proposes that the change of metonymy generates English jokes through the change of ICM (Idealized Cognitive Model). Then a framework of the generation of English jokes is proposed and applied to account for the generation of English jokes. As described in the proposed framework, in metonymy one, Conceptual entity one in ICM one provides mental access to conceptual entity two in ICM one. However, conceptual entity one in ICM one provides mental access to conceptual entity three in ICM two. Accordingly, metonymy one is changed to metonymy two. The contiguity between conceptual entity one and conceptual entity two is the same as the contiguity between the conceptual entity one and conceptual entity three. Due to the change of ICM, metonymy is changed and incongruity comes out. Therefore, it can be inferred that English jokes are generated by the change of metonymy from cognitive linguistics.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".