A Discourse Analysis Study of Comic Words in the American and British Sitcoms
Bibliographic record
Abstract
This paper investigates discourse analysis and its role in studying comic words and their meaning in different contexts. This study aims at showing the relation between discourse analysis and comedy or comic words, presenting the types of comedy and comic words and how they give meaning of mocking, teasing and sarcasm, presenting the difference between mocking, teasing and sarcasm, displaying interjections and their relation with comic words, in addition to analyzing the data in terms of tables. It is hypothesized that many people do not know the meaning of discourse analysis and comedy. Second, they do not have the capacity to differentiate between comedy, mocking, sarcasm and teasing. They also do not know how to use comic words in expressions. Above all, many of them cannot analyze a particular episode properly and people cannot know the intentions of the speaker concerning comic words. The value of this study is for people who are interested in linguistics. The data used in this study are the American sitcom ‘Friends’ and the British one titled ‘Bottom’. The data is analyzed on the basis of the theories of the Cooperative Principle and Taflinger model. The results of “Friends”, the American sitcom and “Bottom”, the British one, show that there are many comic words. The characters use interjections or onomatopoeia to communicate laughter and excitement. The use of teasing is also emphasized by the use of other comic words like: a*s, breasts, and idiot for presenting jokes and laughter sense.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".