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Record W4412568641 · doi:10.14434/ijlcle.v5i.41956

In Pursuit of English: Language and Subjectivity in Neoliberal South Korea

2025· article· en· W4412568641 on OpenAlexaff
Christine Anne McLellan

Bibliographic record

VenueInternational Journal of Literacy Culture and Language Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubjectivityEnglish languageLinguisticsPolitical scienceSociologyHistoryEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

The purpose of the book In Pursuit of English: Language and Subjectivity in Neoliberal South Korea by Joseph Sung-Yul Park is to demonstrate the connection between English and neoliberalism in South Korea through the lens of subjectivity. Park argues that, within the context of South Korea’s neoliberal transformation, English language learning is positioned not only as an economic asset, but also as a moral obligation. It is the moral dimension and affective experience of language users that produces and reinforces both neoliberal power structures and English hegemony in South Korea. This book has a broad scope, addressing neoliberal policies and language education policies at the state level. This book consists of nine chapters, with Park examining the theoretical foundations of neoliberalism (Chapter 2), the historic processes of South Korea’s neoliberalization (Chapter 3), the roles of institutions such as education systems, media, and major corporations influencing language experiences (Chapters 3-6). Following this, Park evaluates the significance of affective stances of anxiety (Chapter 6), linguistic insecurity (Chapter 7), and precarity (Chapter 8). Park uses ethnographic methods and theoretical analysis to make his argument. This book critiques the association of English with neoliberalism purely through the commodification of English and language learning and intervenes by connecting English to neoliberalism through subjectivity and the production of language users as human capital.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.205
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.419
Teacher spread0.409 · 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.

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

Citations6
Published2025
Admission routes1
Has abstractyes

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