MétaCan
Menu
Back to cohort

Theme of immigration in novel ‘The Immigrant’ by Manju Kapur

2023· article· en· W4385762473 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Applied Research · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsPatriarchyImmigrationTheme (computing)Identity (music)SociologyGender studiesNew WomanContemporary societyAestheticsSocial scienceLawArtPolitical science

Abstract

fetched live from OpenAlex

Manju Kapur is one of Indian prominent female writers. Her legacy of writing for woman’s issues has fascinating way for inspiring contemporary writers. This research paper inspires woman who believes herself as meek and submissive. Kapur writes about woman’s socio-cultural problems in male dominated society. Her woman protagonists establish self-image in the society to prove woman’s real strength. Woman has no more household chores for family, but Kapur highlights woman as independent for fulfilling her dreams. Big dreams and fame are not only made for men in society, but also for women who want to see their recognition in contemporary society. In earlytime, women are placid and dependent on her father and husband, but now time is changed, women have self-desires and for that they go against patriarchy to achieve her place in the society. Manju Kapur criticizes male domination for destroying woman’s identity and she forcefully declares that woman is not only made for cooking food in kitchen, nurturing children and satisfying her husband, but also for dreaming her desires and establishes her self-identity in society. Here Kapur depicts Nina protagonist of novel ‘The Immigrant’ as self-discovered woman who transforms herself into western culture. She immigrants from old Indian tradition to western culture, immigrant towards self-identity, immigrant towards adapting foreign lifestyle, and immigrant towards independent life in Canada.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.530

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.336
Teacher spread0.272 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Applied ResearchSame topicSouth Asian Cinema and CultureFrench-language works237,207