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Record W4206287253 · doi:10.1093/res/hgab104

Landscaping the Three-Volume Novel: Jane Austen’s <i>Pride and Prejudice</i> and <i>Mansfield Park</i>

2022· article· en· W4206287253 on OpenAlexfundno aff
Kandice Sharren

Bibliographic record

VenueThe Review of English Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPridePlot (graphics)Prejudice (legal term)NarrativeSociologyPerspective (graphical)AestheticsArtLiteratureLawPolitical scienceVisual artsMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract The importance to Jane Austen’s writing of the picturesque theory that emerged in the second half of the eighteenth century has been well established, from how it influenced stylistic choices to the commentary she offers on land ownership. This article reconsiders the relationship between picturesque theory and plot as it relates to the material forms that shaped the novel in the early nineteenth century, namely the three-volume structure that became the norm in the 1810s. This article argues that, in Pride and Prejudice and Mansfield Park, the metaphorical landscape of the plot is shaped in response to this three-volume structure; considering her novels through the lens of prospects and boundaries that correspond to volumes reveals the extent to which her narrative practice responds to the increasingly standard three-volume structure. But whereas the plot of Pride and Prejudice is naturalized within the structure of its volumes, resulting in a plot that conforms to the picturesque’s aim of concealing artifice, Mansfield Park uses landscapes and geographical locations to limit the reader’s perspective, resulting in an abrupt conclusion that draws attention to the boundaries of the book, thereby denaturalizing the plot within it and the ubiquity of the marriage plot more generally.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.404
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.031
GPT teacher head0.238
Teacher spread0.207 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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