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Record W4387414051 · doi:10.54513/joell.2023.10303

SENSE OF ALIENATION AND EMOTIONAL ESTRANGEMENT –A COMPARATIVE STUDY OF ANITA DESAI AND MARGARET ATWOOD’S HEROINE

2023· article· en· W4387414051 on OpenAlexaboutno aff
K Ragamayee, Dr.K.Lalitha Bai

Bibliographic record

VenueJournal of English Language and Literature · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsAlienationPsychoanalysisPsychologySociologyArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

The quest for identity and the struggle to identify oneself with the surroundings resulting in a sense of alienation and estrangement is experienced by the protagonists of the women's writing.This paper attempts to focus on the common features of the characters in the novels of the two women authors Anita Desai and Margaret Atwood.Most of the protagonists of Desai can be classified under two categories, one with disturbed, obsessed, oversensitive, aesthetic sensibility while the others are a little tough, stubborn, sarcastic, and cynical about the situations and the people around.Whether it is Maya, Monisha, Sita and Tara who belong to the first category or Nirode, Amla, Nanda and Sophie who belong to the second category, it all begins with the feeling of that estrangement which leads to the zenith though they chose different endings.Margaret Atwood opines that the central reality of Canada or Canadian literature depends wholly on the concept of survival and existence.Similar to those of Desai, Atwood's heroines attempt to discover their inner self as they feel that they are like emotional refugees seeking shelter in a terrain that they cannot identify themselves with.Most of her characters, especially women feel emotionally maimed and undergo a constant turmoil to connect with the now and present.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

Explore more

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