MétaCan
Menu
Back to cohort
Record W3217606849 · doi:10.32920/ryerson.14668140.v1

Enacting Ethnicity of “Japanese Canadian” in Oral History: A Comparison Between the Japanese Canadian Sansei and the Ijusha Nisei

2021· preprint· en· W3217606849 on OpenAlexaffabout
Shunya Kawai

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsToronto Metropolitan University
FundersJonsson Comprehensive Cancer CenterYonsei University
KeywordsMulticulturalismIdeologyNegotiationEthnic groupGender studiesIdentity (music)CitizenshipIdentity negotiationSociologyOral historyCollective identityImmigrationPolitical scienceAnthropologySocial scienceAestheticsLaw

Abstract

fetched live from OpenAlex

This paper presents the non-essentializing analysis of ethnic identity formation in comparative research between two groups in the Japanese Canadian community: the Japanese Canadian Sansei and the Ijusha Nisei. Using an oral history approach to understand the development of ethnic identity, I discuss how the social assignment of “otherness” based on the corporeal difference has negatively influenced identity formation in both groups. My comparative analysis further uncovers some of the different strategies that each group takes against the racializing process. Whereas the Japanese Canadian Sansei claim their cultural citizenship in the history of Japanese Canadians by aligning their own personal past with the collective memory of Japanese Canadians, the Ijusha Nisei negotiate it by entitling themselves as a contemporary representative of the ideology of multiculturalism. Finally, understanding the different processes of ethnic identity formation and strategies of negotiation for social inclusion, I discuss the effects of the ideology of multiculturalism on cultural citizenship among Japanese Canadians.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.081
GPT teacher head0.267
Teacher spread0.186 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicOral History, Memory, Narrative AnalysisFrench-language works237,207