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Faulkner’s Gothic Complex in A Rose for Emily

2010· article· en· W2149780249 on OpenAlexvenueno aff
Feng Bei

Bibliographic record

VenueCross-cultural communication · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)DepictionArtPlot (graphics)Style (visual arts)LiteratureHumanitiesArt history

Abstract

fetched live from OpenAlex

A rose for Emily is regarded as a typical gothic novel by Faulkner, due to its odd plot and ghastly setting. This paper discusses the relationship between this novel and the typical gothic novel from the perspectives of the theme, depiction of characters, setting and construction of the plot, so as to search for Faulkner’s gothic complex. Finally the conclusion is drawn that the application of gothic style into this novel helps to highlight its theme and setting. Key words: Goth, theme, depiction of characters, setting, construction of plot Resume Une Rose pour Emily est considere comme un roman typiquement gothique par Faulkner, grace a son intrique extraordinaire et le fond horrible. Cet article traite les rapports entre ce roman et le roman typiquement gothique dans les perspectives du theme, de la description des personnages, du fond et de la construction de l’intrigue, de sorte a trouver le complexe gothique de Faulkner. Finalement, on peut en conclure que l’application du style gothique dans ce roman contribue a approfonfir son theme et accentuer son fond. Mots cles : gothique, le theme, la description des personnages, le fond, la construction de l’intrigue 摘 要 《獻給愛米麗的玫瑰》因其恐怖離奇的故事情節及陰森詭譎的背景氛圍而被譽為福克納的典型的哥特小說之一。本文從故事主題、人物形象塑造、背景及情節構造四方面探討該故事與典型哥特小說的聯繫,找出福克納的哥特情結。由此得出,哥特風格的使用有助於深化故事主題,強化故事的背景與氛圍。 關鍵詞:哥特;主題;人物塑造;背景;情節構造

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.374
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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