Constructing the Global Diversity or Reproducing the Orientalist Gaze: Evaluating Identity Options and Cultural Elements in an English Intercultural Communication Textbook
Bibliographic record
Abstract
English is often ideologically constructed as a global language to facilitate intercultural communication between people of diverse cultural backgrounds. However, it still remains unknown to what extent English learning can enhance English learners’ awareness of global diversity. Given the dominant population of English learners in China, it is of great significance to investigate how English learning might facilitate Chinese learners’ global vision and cultivate their intercultural competence. Seeing language textbooks as a key site of cultural and linguistic representation, this study scrutinizes the hidden ideologies discursively constructed in an English Intercultural Communication (EIC) textbook targeting Chinese English learners. Data are collected from dialogues, case studies, reading passages, cultural notes, exercises in the textbook. Informed by concepts of orientalism and banal nationalism, the study reveals that the distribution of characters is nation-based, essentialized, and even stigmatized. There is an inconsistency between the discursive construction of English as a global language and the actual representation of USA/UK-centered ideology. Chinese and other non-English learners are linguistically and culturally subjected to orientalist interpretation. The internal orientalist representation of Chinese speakers is also reproduced within the diverse backgrounds of Chinese population. Based on the findings, we argue that the simplified, unbalanced and unequal representations of cultural elements may hinder English learners’ awareness of cultural diversity. The study suggests that a more diversified representation of cultural practices should be adopted in EIC textbooks to cultivate the global citizenship through English language education.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".