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Record W171234312

Voices of Four Generations: A Story of the Japanese Canadian Community from Issei to Yonsei

2013· dissertation· en· W171234312 on OpenAlexaboutno aff
Alexis Spieldenner

Bibliographic record

VenueDukeSpace (Duke University) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsGenealogyHistoryGeography
DOInot available

Abstract

fetched live from OpenAlex

This study examines the overall transformation of Nikkei, or individuals of Japanese descent, in Canada from first-generation Issei to fourth-generation Yonsei by drawing on the voices of each generation. How have the Japanese in Canada—once deemed “inassimilable”— transformed into one of the smallest and, statistically, most “assimilated” visible minorities in all of Canada with an intermarriage rate surpassing 95 percent? I examine the reasons behind this phenomenon by interweaving my own family narrative within the larger historical framework. The transformation of the Japanese Canadian community is examined in three distinct stages. The first chapter examines the arrival of the first-generation Issei and the creation of a transnational community in Canada. The second chapter explores the destruction of the transnational community, using the internment experience during World War II as a distinct event responsible in large for the distancing of Japanese Canadians from their “Japaneseness.” Lastly, the third chapter examines how the Canadian government’s “repatriation or resettlement” policy forcibly dispersed the community and accelerated their “blending” into mainstream society. Ultimately, my study asks if it is possible for current and future generations of Nikkei to re-member a Japanese Canadian transnational community. My thesis integrates oral histories of my family members, as well as archival material from Library and Archives Canada (Ottawa) and McMaster University (Hamilton).

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
Teacher spread0.192 · 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 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
Published2013
Admission routes1
Has abstractyes

Explore more

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