Foreign Language Education as Glocal Capital: Statements of Educational Outcomes on China’s Double First-Class University Websites
Bibliographic record
Abstract
The study draws on the Bourdieusian theory of capital to examine the way China’s Double First-Class (hereinafter referred to as “DFC”) universities capitalize on the values of foreign language education. Based on the content analysis of the representations of the officially published and accessible websites of 42 DFC universities, this thesis reports on a qualitative inquiry on the multilingual ideologies of China’s foreign language education in the context of China’s increasing global status. Findings show that China’s DFC universities advertise as ideal “glocal capital” providers for students and construct their foreign language education as a medium for achieving the global vision and local values. Findings also reveal the shifting paradigm of China’s foreign language education from previously orienting towards West-European and Anglophone languages to including the languages of peripheral countries in response to China’s socioeconomic transformations and global development. The study highlights the emerging patterns of China’s foreign language education, which is geopolitically motivated but unequally distributed in educational resources. The study is closed with some implications for enhancing China’s foreign language education and cultivating quality language talents for China’s global and local markets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".