Bibliographic record
Abstract
The motivation and purpose of choosing this topic are to probe and investigate George Bernard Shaw’s ironic and sarcastic tone in the play, Mrs. Warren’s Profession, and how irony and sarcasm are skillfully used by George Bernard Shaw to reveal the theme of prostitution as an antisocial profession and the society’s complicity in its own evils. To have a better analysis of the formal features: Irony and sarcasm, this study tries to approach this play from a formalistic perspective, to add something to our understanding of the writing techniques of this play with the assistance of the methodology of “close reading”. The term irony and sarcasm are in detailed explanation of examples extracted from the play. Four major types of irony are discussed in this article: verbal irony, situational irony, attitudinal irony and dramatic irony. Verbal irony in its most bitter and destructive form becomes sarcasm, in which the speaker condemns someone by pretending to praise him or her. Through the analysis, the article finds out that how the four types of irony together with sarcasm, work together to help author’s characterization and bring out the theme of the play: hypocrisy and injustice of the social reality and its complicity in its own evils. Irony and sarcasm are two of the major writing techniques Bernard Shaw has adopted in this play. The versatile use of them can help forge his dramas, combine moral passion and intellectual conflicts, experiment with symbolic farce, and bring into the spotlight the contemporary issues. Irony and sarcasm are equal to his sharp pen, therefore the investigation on irony and sarcasm can benefit us both as readers and writers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".