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Record W3008937627 · doi:10.5539/ells.v10n1p86

An Analysis of Pride and Prejudice from Structuralist Perspective

2020· article· en· W3008937627 on OpenAlexvenueno aff
Jinhua Zhang

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPrideTheme (computing)Prejudice (legal term)Plot (graphics)Perspective (graphical)Meaning (existential)EpistemologySociologyFeminismComprehensionLinguisticsComputer sciencePsychologySocial psychologyArtificial intelligenceGender studiesPhilosophyMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.266
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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