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Record W4220773143 · doi:10.5430/wjel.v12n2p169

Semiotic Analysis of Train in Jhumpa Lahiri’s The Namesake

2022· article· en· W4220773143 on OpenAlexvenueno aff
Tribhuwan Kumar

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaSemioticsSymbol (formal)TrainPoint (geometry)Plot (graphics)Variety (cybernetics)Computer scienceMaturity (psychological)PaintingAestheticsHistorySociologyArtArt historyArtificial intelligenceLinguisticsPhilosophyLawArchaeologyGender studiesMathematics

Abstract

fetched live from OpenAlex

This paper makes a semiotic analysis of train in The Namesake, a novel by Jhumpa Lahiri who has been marked as a crafty painter of the sensibilities of Indian diaspora. The paper objectifies the thoughtful and vivid use of train by the novelist to match the emotional aspects involved with the plot. Lahiri has just not used trains as a means of transport but as a symbol to the sensibilities of diaspora. The paper commences with a small discussion on general purposes for which writers use trains in their works to project different concerns and later projects the vivid usage of train in the novel. There appears a variety of references to train in The Namesake, each pointing to the writer’s different motives behind its induction. While at a point train shapes a person’s career, at other instance it ruins life. At some point it emerges as a symbol of faith, at another point it leads to deception. Through train, Lahiri projects kaleidoscopic images of the sensibilities of Indian diaspora in addition to gradual change and maturity. She is successful in associating train with emotional contours of her characters.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.998

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)0.0030.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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