Language Learning as a Struggle for Distinction in Todayâs Corporate Recruitment Culture: An Ethnographic Study of English Study Abroad Practices among South Korean Undergraduates
Bibliographic record
Abstract
Young adults in South Korea are encouraged to constantly develop their skills and qualifications to meet the challenges posed by the job market in the country’s neoliberal post-IMF crisis economy. This paper examines the ways in which changes in South Korea’s labor market and corporate recruitment culture have affected the ideologies and practices of the country’s youth with regard to the English language. By drawing on Bourdieu’s concept of distinction and specifying the processes of distinction into replacement, opposition, and addition, this paper clarifies the ideological construction and effects of oral communicative competence in English through an ethnographic analysis of post-secondary learners studying English in a study abroad context. Influenced by South Korea’s recruitment culture, these learners distinguish primarily between learning English for standardized tests in South Korea and learning English for authentic communication while studying abroad. However, the efforts of learners who have studied abroad to develop their oral English skills bear limited fruit in South Korea’s recruitment culture, which does not fully appreciate the value of the job seeker’s experience of having studied English abroad. Thus, the limits of distinction function to impose the burden of English learning on individual learners.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".