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Introduction

2009· book-chapter· en· W938902193 on OpenAlexaff
Katherine Binhammer

Bibliographic record

VenueCambridge University Press eBooks · 2009
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

The repetition of a story at a particular moment in time – in the case of this book, the story of seduction in the later half of the eighteenth century in Britain – prompts at least two different interpretations of how history relates to narrative. The same story might be repeatedly told in order to popularize and naturalize a new historical idea, foregrounding a relation of similitude and emphasizing the mimetic or didactic function of narrative. Or the repetition of a story could denote difference where the deviations within similarity point to a dynamic relation between material conditions and imaginative narratives; in this case, the fact of a story's repetition would indicate both that changing historical conditions open up new objects of understanding and that narrative helps to constitute and to resolve conflicts posed by those new objects. The Seduction Narrative assumes the second formulation to explain how history and narrative interact in the “later eighteenth-century's preoccupation with seduction,” as one historian names the obsessive retelling of the tale. The plot of seduction – where a virtuous young heroine is seduced into believing her lover's vows – dramatizes women's consent to sex at a historical moment when, for the first time, women have “a right to a heart,” as Clarissa boldly claims. The period in Britain under study (1747–1800) witnesses the emergence of companionate marriage as a dominant cultural ideal and this revolution in the history of love carries with it a new social and cultural imperative for women to know their hearts and make choices based upon those affective truths.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.614
Threshold uncertainty score1.000

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 head0.165
Teacher spread0.148 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2009
Admission routes1
Has abstractyes

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