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Record W6930650482 · doi:10.5281/zenodo.13358394

Living as Zainichi in the Twenty-First Century: Identity and Citizenship in Japan's Ethnic Korean Community

2021· article· en· W6930650482 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScapegoatingEthnic groupCitizenshipScholarshipPoliticsPeninsulaPopulationPerspective (graphical)Face (sociological concept)

Abstract

fetched live from OpenAlex

While much of the existing academic scholarship on Japan-Korea relations has focused on the ongoing political and historical disputesrelated to World War II, this paper analyzes the experience of ethnic Koreans living in Japan on a microeconomic and sociological level. Zainichi Koreans and the ongoing structural societal and economic challenges they face in Japanese society are analyzed from a historical perspective throughout this paper. Through a comparative- historical analysis of the experience of Koreans from the point when Japan annexed the Korean Peninsula in 1910 until liberalizing reforms of the twenty-first century, this paper shows that the socialrights, civil rights, and economic opportunities of Zainichi Koreans have remained fluid throughout much of the twentieth century. While the challenges faced by Japan’s Korean population have marginally improved since liberalizing reforms in the 1990s, this paperdemonstrates that ongoing scapegoating on the part of politicians and negative public perceptions of Zainichi Koreans continue to pose challenges to Japan’s sizeable Korean minority population.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0050.005
Open science0.0000.006
Research integrity0.0010.002
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 head0.079
GPT teacher head0.320
Teacher spread0.241 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicComputational Drug Discovery MethodsFrench-language works237,207