AN ANALYSIS OF HUMOR IN J.M BARRIE’S PLAY “THE OLD LADY SHOWS HER MEDALS” ON EFL LITERATURE CLASS
Bibliographic record
Abstract
People tend to find beauty in their life. They create such as painting, dance, music, literature and etc. As one of arts forms, literature is different from other forms of art. It is work writing. People read it for pleasure. They do it in order to know about human life, including its problems, customs, habits, ambition and desires. The data of the study was taken by some following procedures: 1) quoting, means that the writer quoted the dialogue and the stage directions of the play which contains humorous expressions. 2) classifying, means that the writer classified the data into the five groups according to the types of humor such as “wit”, “sarcasm”, “satire”, “irony”, and “repartee”. For the analysis the writer used aspects of drama or play to analyzing it. The aspects of play which is used in analyzing the data are synopsis, characterization (how the characters are described and how they behave), and the language (how the characters speak and how humor is reflected in the play). The writer attempted to understand the meaning of the play and how the expressions of humor employed in the play. He identified sentences that represent characters’ feeling and expressions. Last but not least, the writer used descriptive analysis method. Since the descriptive method is used in the literal sense of describing situations or events. The writer used the descriptive analysis to report the result of the analysis from the data taken. In this case he described expressions of humor which are found in the play
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".