Language Travels and Global Aspirations of Korean Youth
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".