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‘Male’ and ‘Female’ Novels? Gendered Fictions and the Reading Public, 1770–1832

2013· reference-entry· en· W2201588007 on OpenAlexaff
Barbara M. Benedict

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsTrinity College
Fundersnot available
KeywordsDelicacyReading (process)EuphemismDerogationLiteraturePopular fictionFeelingFemininityPerspective (graphical)EtiquetteGender studiesPlot (graphics)Period (music)TabooSociologyArtHistoryPsychologyAestheticsPhilosophySocial psychologyVisual artsAnthropologyLinguistics

Abstract

fetched live from OpenAlex

The fiction from 1770 to 1830 shows the strains of a society that increasingly identified cultural consumption with gender. Whereas the sentimental novels of the 1770s used epistolary narrators to relate stories of love and feeling from the perspective of both men and women, by the 1790s the new, Gothic novels were centred on women besieged by tyranny from without and uncertainty from within. This genre fiction contributed to the derogation of the novel and its association with an undiscriminating female audience. Throughout the period, women were held up as the quintessential novel-readers because more women were visibly writing and reading novels than ever before, and because the popular marriage plot, female hero, thematic focus on etiquette, and emphasis on delicacy and refinement all seemed to speak to feminine concerns. In fact, most novel-writers and novel-readers were men because men wrote and read more than women.

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)0.002
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.014
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0110.012
Scholarly communication0.0100.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.053
GPT teacher head0.224
Teacher spread0.171 · 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
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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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