FIGURATIVE LANGUAGE IN ENGLISH STAND-UP COMEDY
Bibliographic record
Abstract
This descriptive qualitative research was about the analysis of figurative language in English stand-up comedy. The purposes of this study were to identify the types of figurative language and to describe the functions of figurative language found in the selected video of stand-up comedy show. The data source was taken from one of selected videos of Russell Peters stand-up comedy show. Russell Peters’s speech contained about figurative language in the video is observed. The data were collected through content analysis technique by collecting the verbal language used by Russell Peters. The first research questions was analyzed by McArthur (1992) theory and supported by Crystal (1994) theory to find out the types of figurative language found in English stand-up comedy. To answer the second research questions about the functions of figurative language found in English stand-up comedy was analyzed by Chunqi (2014) theory and suppoted by Kokemuller (2001) theory and Turner (2016) theory. After analyzing data, it was found that Irony was the most dominant figurative language used by Russell Peters in “Russell Peters Comedy Now! Uncensored” with 29.94%. It was happened because the kind of topics used by Russell Peters in that show were about ethnics (canadian, white people, black people, brown people and asian), society case (beating child) and culture (accent and life style of various ethnics in the world, habitual of various ethnics in the world). Irony and Hyperbole were needed dominantly in the performance, to entertain the audiences in the stand-up comedy show. The function of eleven types of figurative language which were used by Russell were concluded. The functions were to amuse people in comedic situations, to expand meaning, to explain abstract emotions, to make sentence interesting represented and give creative additions. Keywords: Figurative Language, Stand-Up Comedy, English Stand-Up Comedy
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".