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Record W2612558830 · doi:10.33776/candb.v6i1.3078

Representations of Home in Obasan and Nihonjin: The Issei, Nisei, Sansei of Canada and Brazil

2017· article· en· W2612558830 on OpenAlexaffabout
Cecily Raynor

Bibliographic record

VenueCanada and Beyond A Journal of Canadian Literary and Cultural Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

Joy Kogawa's Obasan (1981) and Oscar Nakasato's Nihonjin (2011) are two novels that narrate the lives of Japanese diaspora in Canada and Brazil respectively. Both countries share a rich tradition of Japanese migration during the late 19 th and early 20 th centuries, which, while distinct in important ways, has resulted in a significant body of cultural production. In this article, I examine spatiotemporal treatments of the home space in Obasan and Nihonjin, arguing that this realm serves two primary functions: 1) as a site for the expression and construction of identity 2) as a point of disruption and resistance in the face of cultural, linguistic and national alienation. Examining Japanese migration literature from a comparative lens sheds light on the past as a gateway to the present, while nuancing national discourses around race and ethnicity. Although these two narratives are separated by thousands of miles and thirty years in their publication dates, Japanese migrant tales from Brazil and Canada teach us diverse and intersecting lessons about ethnic heritage and cultural plurality. The domestic space is particularly critical for observing these crossings as its attention to language, food culture and other artifacts reveals a multi-layered experience.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.949

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 studies0.0010.001
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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