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Record W3114043227 · doi:10.1515/multi-2020-0034

“We contribute to the development of South Korea”: Bilingual womanhood and politics of bilingual policy in South Korea

2020· article· en· W3114043227 on OpenAlexaff
Bong-gi Sohn, Mi Ok Kang

Bibliographic record

VenueMultilingua · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCognitive reframingGovernment (linguistics)EntrepreneurshipLanguage policyNegotiationNationalismIdentity (music)SociologyPoliticsPolitical scienceMultilingualismGender studiesLinguisticsSocial sciencePsychologyPedagogySocial psychology

Abstract

fetched live from OpenAlex

Abstract Global flows of migration to South Korea bring a new challenge of how to negotiate the identities of migrants. Unlike other reported cases that reframe the value of migrants’ first language as part of contingent practices of diversity management, the South Korean government has responded to this challenge by explicitly reframing so-calleddamunhwamothers (foreign women married to Korean men) as bilingual workers, imagining them as self-governed, autonomous workers whose linguistic capital can be mobilized for the betterment of South Korean society. The government’s adoption of linguistic entrepreneurship and ethnocentric nationalism becomes particularly salient in this process. This paper studies how fourdamunhwamothers respond to this new bilingual worker identity as promoted in the bilingual policy texts. We examine the ways in which they negotiate their bilingual worker identities by echoing the government’s new linguistic nationalism and linguistic entrepreneurship on the one hand, and by problematizing the insecure job markets, stratified linguistic needs, lack of systematic training for bilingual instructors, and native Korean’s misunderstanding of their new roles on the other. Finally, we discuss the implications of Korea’s bilingual policy, elaborating on the significance of linguistic entrepreneurship in language policy planning and practice and calling for more reflective accounts of ecological and translingual policy implementation in Korea.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0140.009
Scholarly communication0.0050.003
Open science0.0000.006
Research integrity0.0010.002
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 head0.044
GPT teacher head0.321
Teacher spread0.277 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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