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Record W2759592751 · doi:10.3968/9703

Portraits d’Une Femme: A Comparative Study of Jean Rhys’s Antoinette and Charlotte Brontë’s Bertha

2017· article· en· W2759592751 on OpenAlexvenueno aff
Leila Hajjari, Hossein Aliakbari Harehdasht, Yasaman Mirzaie

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDecadence, Literature, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsSargasso seaOppressionPortraitColonialismLiteratureArtMotif (music)HistoryArt historyArchaeology

Abstract

fetched live from OpenAlex

Reading Jean Rhys’s Wide Sargasso Sea  after Bronte’s Jane Eyre , one does not sympathize with Jane anymore, nor does she really see Bronte’s Bertha as an imbruted partner for Mr. Rochester. This paper will take a comparative  look at the way Antoinette Cosway is presented and treated in Jean Rhys’s Wide Sargasso Sea  and at the way Bertha is presented in Charlotte Bronte’s Jane Eyre . The study of some dominant themes in Rhys’s novel, themes such as racial discrimination, imperial oppression, place attachment, displacement and its influence on Antoinette, will work as technical elements of the comparison. In particular, the motif of Antoinette/Bertha’s madness in an imperialistic and patriarchal society will be analyzed in details. The scholars who are interested in post-colonialism will find this paper useful in that it discusses the role of the colonizer and the colonized with regard to the female characters of the putative novels.

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)0.006
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: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0260.016
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.043
GPT teacher head0.392
Teacher spread0.349 · 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
Published2017
Admission routes1
Has abstractyes

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