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Record W4221023440 · doi:10.5430/wjel.v12n2p72

Discourse of Psychoanalytic Insight and the Sufferings of Immigrants in Chitra Banerjee Divakaruni’s The Mistress of Spices

2022· article· en· W4221023440 on OpenAlexvenueno aff
C. G. Karthikadevi, C. Jothi

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryPsychoanalytic theoryMythologyLiteratureImmigrationMAGIC (telescope)MemoirMagic realismHistorySociologyArtPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

South Asian novelist Chitra Banerjee Divakaruni is one of the most famous diasporic writers. She is also a great short-story writer, poet, and essayist. Her books have been translated into 29 languages including Hebrew, Dutch and Japanese. Her themes are relevant to South Asian Diasporic experience, History, Myth, Magic Realism and Cultural Diversity, Women Immigrants etc. Her works largely set in India and United States. There may be a galaxy of women writers. Most of her works give the insight and lively experience to the readers. Her poetic language in the text is far more appreciable. The reader may fall in love with the way of her expression and her beautiful poetic way of writing. She explores all her immigrant experiences through her writing. She gives life to her stories and fiction in such an excellent manner. She expresses her own pain and suffering especially through her women characters. Many autobiographical incidents are employed by her. So that she is distinguished from all other immigrant writers. Most of her works deal with the images of Bengali customs and habits. This paper is an attempt to deal with the psychoanalytic perspectives of the characters in Mistress of Spices and the predominant role of culture which focuses traumatic and sufferings of immigrants.

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)0.001
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 score0.028
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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
Published2022
Admission routes1
Has abstractyes

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