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Record W2902596426 · doi:10.3138/jcs.2018-0008

Assimilation—On (Not) Turning White: Memory and the Narration of the Postwar History of Japanese Canadians in Southern Alberta

2019· article· en· W2902596426 on OpenAlexvenueaboutno aff
Darren J. Aoki

Bibliographic record

VenueJournal of Canadian Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismNarrativeMainstreamHistoryWhite (mutation)SociologyGender studiesCollective memoryRacial hierarchyRace (biology)Political scienceLiteratureLawArchaeology

Abstract

fetched live from OpenAlex

This article explores understandings of “race”—specifically, what it means to be Japanese—of nisei (second generation) individuals who acknowledge their near complete assimilation structurally and normatively into the Canadian mainstream. Examining historically contextualized analyses of memory fragments from oral history interviews conducted between 2011–17, the article focuses on the voices and experiences of southern Alberta, an area whose significance to local, national, continental, and trans-Pacific histories of people of Japanese descent is belied by a lack of dedicated scholarly attention. In this light, the article reveals how the fact of being Japanese in the latter half of the twentieth century was strategically central to nisei lives, both as individuals and in their communities. In imagining a racial hierarchy whose apex they knew they could never share with the hakujin (whites), the racial heritage they nevertheless inherited, and would bequeath, could be so potent as to reverse the direction of the colonial gaze with empowering effects in individual engagements then and as remembered now. We see how the narration and validation of one’s life is the navigation of wider historical contexts, the shaping of the postcolonial legacy of imperial cultures as Britain and Japan withdrew from their erstwhile colonial projects 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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 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
Published2019
Admission routes2
Has abstractyes

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