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Record W2781922661 · doi:10.3138/ecf.30.2.223

“He bears no rival near the throne”: Male Narcissism and Early Feminism in the Works of Charlotte Dacre

2018· article· en· W2781922661 on OpenAlexvenueno aff
Jennifer L. Airey

Bibliographic record

VenueEighteenth-Century Fiction · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
Fundersnot available
KeywordsPatriarchyFeminismGender studiesAgency (philosophy)MoralitySociologyThroneNarcissismValue (mathematics)PoliticsPsychoanalysisPsychologyLawPolitical scienceSocial science

Abstract

fetched live from OpenAlex

For many critics, Charlotte Dacre is an essentially conservative author, whose female villains ventriloquize and thereby discredit early feminist thought. This essay disputes such readings by exploring her under-acknowledged criticisms of patriarchy. Dacre’s male characters are self-important narcissists, who disdain female education for the agency it gives women. If a woman is well-educated, she is less likely to submit, less likely to serve as an extension of her husband’s will alone. Patriarchy also encourages women to turn on one another, Dacre argues; her female villains uniformly value advancement among men over sisterhood, and they willingly destroy other women to attain their own ends. Dacre thus takes a pessimistic approach to women’s options at the beginning of the nineteenth century. Her female characters do not enjoy happy endings, regardless of marital status, level of education, or morality, because patriarchal systems are designed to crush women no matter how they behave. Far from mocking early feminism, then, Dacre criticizes it for being insufficiently radical in addressing problems with male behaviour.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.033
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.245
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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