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Record W3031983324 · doi:10.1080/15348458.2020.1753195

Meaning-Making Process of Ethnicity: A Case of Japanese Mixed Heritage Youth

2020· article· en· W3031983324 on OpenAlexaff
Naoko Takei

Bibliographic record

VenueJournal of Language Identity & Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHeritage languageAcknowledgementEthnic groupMeaning (existential)SociologyCultural heritageGender studiesLinguisticsAnthropologyPsychologyHistoryPedagogyArchaeology

Abstract

fetched live from OpenAlex

This article aims to provide a better understanding of Japanese Mixed Heritage Youth’s (JMHY) relationship to heritage language and senses of ethnicity, by analyzing their daily language use as provided by 14 JMHY. All (with two exceptions) do not use Japanese at home, and some have enrolled in Japanese as a second language class at a university. The article suggests that the participants’ use of the term “half Japanese” to describe their ethnicity reveals their complex relationship with their Japanese heritage. Despite the ways JMHY differentiate themselves from Japanese people, the term indicates their recognition of a connection to Japanese heritage and the acknowledgement of the coexistence of the other half of their heritage at the same time. Engaging in making meaning of their ethnicity, JMHY show a tendency to return to roots and to rely on traditions, but it is not necessarily a one-way direction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0260.015
Scholarly communication0.0070.005
Open science0.0020.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.470
Teacher spread0.401 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2020
Admission routes1
Has abstractyes

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