Globalization of English teaching and overseas Koreans as temporary migrant workers in rural Korea
Bibliographic record
Abstract
One of the most prominent impacts of neoliberal globalization on language is the rise of the importance of English ( Heller 2003 ; Phillipson 2003 ). In today's globalized economy, struggles over the resources of English language education tie English to processes of construction and reproduction of social differences and inequality ( Heller 2002 ). Korea's newly launched Teach and Learn in Korea (TaLK) program is one such example. The TaLK program recruits native speakers of English, including overseas ethnic Koreans, as temporary immigrant workers to teach English to rural elementary students. Using the concept of ‘language management,’ this article demonstrates how the Korean government views transnational Koreans’ ethnicity as an asset, while treating their linguistic resources as manageable commodities. Analyses of policy documents, media coverage, and essays by and interviews with TaLK participants reveal how the TaLK program may contribute to sustaining social differences and inequality in multiple ways, although the program's main goal is to provide equal opportunities to rural students. 세계화가 언어에 미치는 영향 중 가장 대표인 현상 중 하나는 영어의 중요성의 부각이다. 세계화되어 가고 있는 경제 체제 하에서 영어 교육을 위한 자원 확보 경쟁이 영어와 사회 불평등의 관계를 심화시킨다. 이러한 관계를 보여 주는 일예로 한국 정부가 새롭게 도입한 정부 초청 해외 영어봉사 장학생 (TaLK) 프로그램을 들 수 있다. 이 프로그램을 통해 한국 정부는 농촌 학교에서 영어를 가르칠 교포를 포함한 원어민을 모집한다. ‘언어 관리’라는 개념을 도입해 본 논문은 어떻게 한국 정부가 한인 교포 TaLK 참가자들의 민족성을 활용가능한 자원으로 개념화하고 참가자들의 언어 능력을 언어 관리의 대상으로 관리하는지를 보여준다. 정책 문건, 대중 매체, 참가자의 수필, 참가자와의 인터뷰를 분석하여 지역간의 영어 격차를 해소 하기위해 도입한 TaLK 프로그램이 그러한 격차를 유지, 심화시키는데 기여하는 역할을 고찰한다. [Korean]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".