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Record W2585388146 · doi:10.25071/2369-7326.40254

Revisionary Historical Metatext or 'Good Mills and Boon'?: Gender, Genre, and Philippa Gregory's The Other Boleyn Girl

2016· article· en· W2585388146 on OpenAlexaffvenue
V. Logan Kennedy

Bibliographic record

VenuePivot A Journal of Interdisciplinary Studies and Thought · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGirlNarrativeSilenceIdeologyRomanceHistoriographyLiteratureHegemonyHistoryPoliticsSociologyGender studiesArtAestheticsPsychologyLaw

Abstract

fetched live from OpenAlex

In recent decades, literary critics have become increasingly interested in the ways that contemporary historical novels are used to write “history from below.” In On Lies, Secrets, and Silence (1978), Adrienne Rich describes a process she calls “re-visioning,” a process that is defined as “looking back, of seeing with fresh eyes, of entering an old text from a new critical direction” (35). Many female authors of contemporary historical novels engage in exactly this process, looking back in time and reinserting the histories of women into the dominant narrative of history in which they are often excluded or marginalized. Novelists like Philippa Gregory, Diana Gabaldon, and Tracy Chevalier have made feminist politics clearly visible in their bestselling historical novels. And yet, for all their potential and visible disruptions of patriarchal ideology, many of these popular novels also make use of literary archetypes, tropes, and narrative patterns that reinstate hegemonic ideologies about individual identity and social structure. Using Philippa Gregory’s The Other Boleyn Girl (2001) as a case study, this paper argues that popular women’s historical novels often exist in tension between the pulls of revisionary feminist historiography and the popular romance narrative.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.081
GPT teacher head0.371
Teacher spread0.290 · 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
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

Citations2
Published2016
Admission routes2
Has abstractyes

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