Bibliographic record
Abstract
The thesis is going to analyze the technique of light manipulation in the drama Death of a Salesman, aiming to elucidate the function that it plays in character display and the theme revealing. The thesis will cover all the colors of lights that appear in the drama and examine them carefully. Different perspectives will be used, from the comparison among different colors to the comparison of the same color under different context. By all these means, the unique function of the light manipulation perspective in understanding the theme of the drama will be self-proved. Key Words: Death of a Salesman,stage light,colors Resume Cet article analyse la technique d’application des couleur d’eclairage dans le drame Mort d’un commis-voyageur d’ Arthur Miller, dramaturge moderne americain, et son role de demontrer les caracteres des personnages et de reveler le theme. L’article va citer toutes les couleurs d’eclairage apparues dans la piece et les examiner dans de diverses perspectives, de la comparaison de differentes couleurs a la comparaison des functions de la meme couleur dans des contextes differents. A travers cette analyse, l’auteur veut temoigner que cette perspective d’analyse joue un role particulier dans la comprehension du theme de ce drame. Mots cles: Mort d’un commis-voyageur,l’eclairage,les couleurs 摘 要 本文以美國現代戲劇家亞瑟 •米勒的《推銷員之死》作為分析文本，分析其中的燈光色彩的運用技巧，及其對人物性格展示和主題揭示的作用。本文將窮盡劇中所提到的所有燈光色彩，並對它們加以分析，從色彩的對比，同一色彩在不同情境下的不同意義等角度加以闡釋，力圖證明這一分析視角對深入理解該劇的主題有著獨特的作用。 關鍵詞：《推銷員之死》；舞臺燈光；色彩
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".