Bibliographic record
Abstract
In his renowned short story, A Rose for Emily, the master artist William Faulkner verbally paints the portraits of a tragic woman, Miss Emily. Throughout this story, Faulkner creates numerous figurative portraits of Emily, and makes her physical appearance change dramatically. The description of Emily’s changing physical appearance in different periods enables the readers to watch how Emily transforms from a slender lady to an old gloomy “bloated” one, and from an obedient, genteel young girl to a murderer and corpse keeper. This paper just aims to unveil Emily’s interior complexity and internal changes through the analysis of her external changes and at the same time attempts to explore the causes for her changes. Key words: William Faulkner, A Rose for Emily, changing portrait, causes, analysis Resume: Dans cette nouvelle renommee, Une Rose pour Emily, la maitre artistique William Faulkner decrit verbalement le portrait d’une femme tragique, Mlle Emily, et fait changer son apparence physique dramatiquement. La description de l’apparence changeante d’Emily dans de differentes periodes permet aux lecteurs de voir comment Emily se transforme d’une demoiselle splendide en une vieille figure morne et arrogante, et d’une jeune fille obeissante et gentille en meurtriere et garde des cadavres. L’article present vise a mettre en lumiere la complexite interieure d’Emily et ses mutations internes a travers l’analyse de ses changements externes et essaie, en meme temps, d’explorer les causes de ses transformations. Mots-Cles: William Faulkner, Une Rose pour Emily, portrait changeant, causes, analyse
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".