Teaching English for Economic Competiveness: Emerging Issues and Challenges in English Education in China
Bibliographic record
Abstract
Under China’s market economy, English language learning has been adopted as a strategy to promote the nation’s economic competitiveness in a global economy. This development reflects a discourse of linguistic instrumentalism. Based upon individual interviews of 24 English teachers in Zhejiang Province, China, the study reveals that teachers question the assumptions of linguistic instrumentalism, the gatekeeper role of English, the impact of the increasing dominance of English on Chinese language, and their students’ internalization of the belief in the superiority of Anglo culture. In addition, the study suggests that as a result of globalization, the delivery of English education in China has experienced unprecedented marketization and privatization. Despite increases in their salaries, teachers still live in poor conditions. Under the fee-paying principle, parents expect teachers to provide the best service to their children, and as such the relations between teachers and students have become like those between businesses and clients. It seems evident that teaching has been devalued and commodified in the age of market economy.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".