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Record W2141640843 · doi:10.1215/10679847-3125913

Language Travels and Global Aspirations of Korean Youth

2015· article· en· W2141640843 on OpenAlexaboutno aff
Jennifer Jihye Chun, Ju Hui Judy Han

Bibliographic record

Venuepositions asia critique · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsDestinationsEmployabilityContext (archaeology)SociologyGender studiesPolitical sciencePublic relationsGeographyTourismPedagogy

Abstract

fetched live from OpenAlex

The so-called English fever and the international mobility of South Korean youth are linked to the popular pursuit of English-language education in the United States, Canada, Australia, New Zealand, and the Philippines. From the perspective of young overseas travelers, these are short-term, semistructured “international experiences” beyond mere language training, and they are widely perceived as necessary for enhancing one's value and career prospects in the competitive job market back home. Based on an in-depth study of temporary Korean residents in Vancouver, this article explores the everyday experiences and subjective transformations produced by English-language travel overseas. Drawing upon surveys, interviews, and focus groups, the authors find that temporary sojourns to English-speaking destinations such as Vancouver represent more than rational, instrumental strategies to enhance one's future employability and socioeconomic advancement. They constitute a critical evaluative terrain in which Korean youth assess and reassess their mobility strategies, life trajectories, and identities in the context of Korea's seemingly relentless pursuit of individual and national advancement in an economically and racially stratified world order. In this sense, overseas language travels operate as spaces that both reinscribe as well as destabilize existing social hierarchies along race, class, and nation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.043
GPT teacher head0.337
Teacher spread0.294 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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