Linguistic Imperialism and Standard Language Ideology in an English Textbook Used in China
Bibliographic record
Abstract
An increasing number of studies have demonstrated how language textbooks serve as an arena for ideological reproduction. Following the theory of language ideology, this paper aims to examine the ideological representation of English in a textbook targeting Chinese university students in China. Data were subjected to content analysis and critical discourse analysis regarding their reference to the embodied identity options, images, case studies, cultural notes, exercises, dialogues, and reading passages in the textbook. Findings reveals that by highlighting the dominant position of English and simplifying the multilingual landscape, the textbook tends to place English at the center of prominence. The dominance of English is further entrenched by expressions of the values of speaking Standard English. It is argued that the representation of speaking Standard English ideology can be understood as the global penetration of linguistic imperialism. Furthermore, the textbook also reproduces biased gender and class representation of social characters, which might exacerbate learners’ prejudice towards certain groups on the basis of their understandings of real-life power relations. It is hoped that the study can shed some lights on providing English language learners with a more diversified textbooks for cultivating their language awareness and intercultural competence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".