Bibliographic record
Abstract
Pride and Prejudice is a classic novel from Jane Austen, a prominent female British writer, which has attracted considerable attention from the perspective of language, content, feminism, and marriage view but without the plot organization. Different from the previous study, this paper aims at the plot organization of the novel to see its structure and the deep meaning. This paper is devoted to analyzing the novel from the surface and deep structure, in which the structuralist approach is employed. The surface and deep structure theory is the main clue; besides, the structuralist narratological methods are applied to analyze the cases in the novel and explore the surface structure and deep structure respectively. The concepts of surface and deep structure and the structuralist narratological methods were applied to analyze Pride and Prejudice to see how the plots act to serve for the theme. The paper shows that the achievement of a novel is closely related to its complement of the structure. The clear hierarchies can effectively elaborate the story and the theme. To divide the plot into several parts can easily control and handle the development and interaction of the plots. The relative and oppositional relations of the different plots contribute to the demonstration of the theme and the comprehension of the readers.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".