Cultural Differences in the Content of English Textbooks between Indonesia and Japan
Bibliographic record
Abstract
With the increasing demand for English as a lingua franca, Japan and Indonesia have integrated English into their curricula. However, both countries exhibit low English proficiency, highlighting the need for improved English education. In this study we examine cultural differences in English textbooks used in Japan and Indonesia to identify areas for improvement. Using Hofstede’s cultural dimensions as an analytical framework, we conducted a comparative analysis on New Horizon English Course 3 (Japan) and Buku Panduan Guru English for Nusantara (Indonesia). Findings indicate that the Japanese English textbook exhibits higher values for individualism, femininity, long-term orientation, and international content, while the Indonesian English textbook emphasizes collectivism, uncertainty avoidance, short-term orientation, and national identity. These differences reflect broader cultural influences and educational goals. The study suggests that Japanese textbooks could benefit from a more balanced approach between individualism and collectivism, while Indonesian textbooks could incorporate more international content to enhance global awareness. Given the study’s limited scope—analyzing only one textbook per country—future research should expand to multiple textbooks, curricula, and supplementary materials for a more comprehensive understanding of cultural influences on English 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".