Bibliographic record
Abstract
The realistic tragedy ---Long Day’s Journey Into Night marks the climax of Eugene O’Neill’s literary career. This paper explores the tragic sources of Mary’s misfortune from multiple dimensions: current social factors, the internal vulnerability of human nature, the mutual indifference and hurt among family members, O’Neill’s early personal experiences and the dramatist’s viewpoints on tragic destiny. Keywords: Long Day’s Journey Into Night, Mary, tragedy, source Resume La tragedie realistique - Long Day’s Journey Into Night marque l’apogee de la carriere litteraire d’ Eugene O’Neill. Cette these presente essaie de faire une recherche sur les sources approfondies de la tragedie des personnages importants – Marie selon les 5 points d’attitude: les facteurs objectifs sociaux, les faiblesses humaines subjectives, l’indifference et la blessure de la part des membres de famille, les influences de l’experience de vie des premieres annees d’Eugene O’Neill ainsi que son destion tragique. Mots-cles : Long Day’s Journey Into Night, Marie, tragedie, source 摘 要 現實主義悲劇《進入黑夜的漫長旅程》是尤金 •奧尼爾劇作生涯的頂峰。本文試圖多維度探究劇中重要角色-瑪麗悲劇人生的複雜根源：社會因素，人性弱點，家庭成員的隔閡與傷害，奧尼爾早年不幸生活經歷的影響和他的悲劇命運觀。 關鍵詞：《進入黑夜的漫長旅程》，瑪麗，悲劇，根源
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".