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THEMES OF DISPLACEMENT AND IDENTITY OF VIKRAM LALL IN M.G. VASSANJI’S NOVEL THE IN-BETWEEN WORLD OF VIKRAM LALL

2024· article· en· W4403376717 on OpenAlexaboutno aff
Manoj Dongre, Neeta Lalwani

Bibliographic record

VenueShodhKosh Journal of Visual and Performing Arts · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsDisplacement (psychology)Psychology

Abstract

fetched live from OpenAlex

The issue of migration, displacement or dislocation is not new. From the beginning man has been moving across the globe in search of a job or for safety reasons. The In-Between World of Vikram Lall by M.G. Vassanji examines the difficulties of displacement and identity in the post-colonial Kenyan setting, where diasporic peoples' lives are shaped by historical, political, and cultural factors. Vikram Lall, the protagonist is the man divided between several identities and commitments. The journey of Vikram Lall, which reflects the larger story of South Asian immigrants in East Africa is examined in this novel. The story expertly combines themes of identity negotiation in the face of changing socio-political environments, displacement and belonging.In his novel The In-Between World of Vikram Lall Vassanji uses the idea of migration both psychological and physical to emphasize the conflict and confusion that diasporic people go through. The protagonist Vikram battles to settle his mixed identity arose between his colonial Kenyan childhood, his Indian ancestry, and his ultimate banishment to Canada. His journey is placed within the larger framework of Kenyan history and the effects of colonialism, which highlights how the personal and political intertwine to shape diasporic identities in the novel.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.033
GPT teacher head0.373
Teacher spread0.340 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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