The Exploration of the Artistic Methods and Significances of Cartoonised Characters of A Good Man Is Hard to Find
Bibliographic record
Abstract
Flannery O’Connor is one of the 20th Century Southern American writers, whose usual theme of absurdity and alienation is well embodied in her classic short story A Good Man Is Hard to Find. This paper mainly deals with the cartoonised characters, especially the granny. The artistic methods used to describe the characters are closely related to the following: realistic method, exaggerative method, metaphorical method, and symbolic method. This article attempts to explore the artistic methods used to describe the characters and their significances and also their contributions to the theme of absurdity and alienation. Key words: A Good Man Is Hard to Find, cartoonised character, absurdity Resume Flannery O’Connor est une des femmes ecrivains tres connues des Etats-Unis du IIe siecle. Son recueil des nouvelles « A Good Man Is Hard to Find » attire surtout plus d’attention des lecteurs. Il decris des personnages humouristiques tres vives, ce qui se traduit en evidence par la grand-mere. Les methodes de description des personnages caricaturistes( les methodes artistiques) peuvent de resumer en : methodes realistique, methode exagerative, methode metaphorique et methode symbolique. Cette these part des personnages humoristiques et cherche les formes de description et les significations des personnages dans des oeuves pour devoiler le theme d’absurdite. Mots-cles: A Good Man Is Hard to Find, les personnages humoristiques, l’absurdite. 摘 要 弗蘭納裏 •奧康納是 20世紀美國著名女作家。她的短篇小說集《好人難尋》較引人注意，而其中《好人難尋》更是倍受關注。她刻畫了栩栩如生的漫畫式人物，這一特點在老奶奶身上表現得尤為突出。漫畫式人物的表現方式 (藝術手法)可概括為：寫實法，誇張法，比喻法與象徵法。本文擬從小說中漫畫式人物出發，探討此類人物在作品中的表現方式及其意義，從而揭示其荒誕主題。 關鍵詞：好人難尋；漫畫式人物；荒誕
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".