Industrial Corruption： The main culprit for the relationship between Husband and wife in “Odour of Chrysanthemums”
Bibliographic record
Abstract
The relationship between man and woman is one of the main foci of D.H.Lawrence, a famous English writer. On the basis of a careful analysis of his short story--“Odor of Chrysanthemums”, the present writer tries to prove that the industrial corruption is the main culprit for the dead relationship between husband and wife, a major one of the relations between man and woman in human society. Key words: relationship between husband and wife, industrial corruption, main culprit Resume: La relation entre l’homme et la femme est l’un des themes de D.H. Lawrence. A travers l’analyse minutieuse de son roman Odour of Chrysanthemums, l’auteur tente de prouver que la corruption industrielle est le coupable principal de l’aggravation de la relation entre la mari et la femme dans le roman, alors que le relation conjugale est l’une des relations importantes de la societe humaine. Mots-Cles: relation conjugale, corruption industrielle, coupable
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".