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Record W3157070983 · doi:10.3968/12039

For the Drifting Sargasso Finds Its Way: Loss and Reconstruction of Antoinette’s Identity in Wide Sargasso Sea

2021· article· en· W3157070983 on OpenAlexvenueno aff
Aminur Rashid

Bibliographic record

VenueStudies in literature and language · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Sargasso seaIdentity crisisStyle (visual arts)State (computer science)LiteratureAestheticsHistorySociologyPhilosophyComputer scienceArtGeologySocial scienceOceanography

Abstract

fetched live from OpenAlex

Adopting the applied techniques and methods of comparative literature, the postcolonial, the feminist and the psychosocial theories, this paper comes in contact with two literary works through spotlighting the external forces that can be supportive and encouraging on the one hand but undermining and discouraging the two protagonists’ long and arduous search for an identity and an independent self on the other. The paper investigates the similarities and differences in these two literary works to discover how the two writers correspond to the search of identity of the two protagonists’ in the two different novels. In addition, this paper also scrutinizes the way of the struggles that these female protagonists display to achieve their respective goals. Upon penetrating into the novels, each protagonist alights on herself in a distinctive manner depending on the state of affairs and the external forces that thoroughly determines the construction or destruction of her identity in full measure. In fact, they have picked what they want. They care to have a world of their own where there shall be their own choices of life style, terms and decisions.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.002
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 head0.018
GPT teacher head0.327
Teacher spread0.309 · 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
Published2021
Admission routes1
Has abstractyes

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