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Record W7113320987

“With this past before you, all around you”: On the Transformation of Identities in M. G. Vassanji’s No New Land

2019· article· en· W7113320987 on OpenAlexaboutno aff

Bibliographic record

VenueTamkang University Institutional Repository (TKUIR) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsAmbivalenceEmigrationPosition (finance)Identity (music)PrecarityDislocationDisplacement (psychology)Dimension (graph theory)
DOInot available

Abstract

fetched live from OpenAlex

This essay analyses how M. G. Vassanji's second novel No New Land (1991), which thematises how Tanzanians of Indian origin emigrate to Canada in the nineteen-seventies and seek to build their new life there, explores the effects of diasporic double dislocation. It considers how the novel's thematisation of diasporic double dislocation illuminates the possibilities and limitations of cross-cultural dynamics. For the purpose, it first examines how the characters identify themselves with East Africa and how the drastic changes caused by decolonisation lead to their sense of diasporic dislocation. It then analyses how their new life in Canada makes them feel further alienated and how they seek to cope with this additional sense of dislocation. Next, the essay considers how Vassanji explores another dimension of diasporic dislocation by making some characters seek to re-define their cultural and communal identity. It concludes by examining the ambivalence of the novel's conclusion in light of Vassanji's own oscillation concerning his cultural position as a postcolonial writer. The novel's ending in which communal unity eventually stifles individual freedom, the essay concludes, reflects the writer's increasing belief in the possibilities of cross-cultural transformation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.993

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.204
Teacher spread0.195 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

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