Bibliographic record
Abstract
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".