Research on Cultural Adaptation and Enhancement Strategies for South Asian International Students in China under the "Belt and Road" Initiative—Taking Pakistan, Bangladesh, Nepal, Sri Lanka, and the Maldives as Examples
Bibliographic record
Abstract
With the advancement of the "Belt and Road" initiative, the number of South Asian students studying in China has continued to grow, and the various challenges they face during their study abroad have become increasingly prominent year by year. Notably, the difficulties encountered in the process of cross-cultural adaptation have emerged as a significant issue that cannot be ignored in the research and development of international student education in Chinese universities. Based on a literature analysis method, this study explores the cultural backgrounds and characteristics of South Asian students studying in China, analyzes the challenges they may face in aspects such as social life, academic adaptation, mental health, and interpersonal relationships, and examines the cultural difference roots of these issues. Finally, the study proposes measures such as conducting language and cross-cultural adaptation training, establishing international student associations, improving the care system, and reforming educational management models to enhance the cross-cultural adaptation level of South Asian students. The aim is to improve their learning quality and living standards in China and promote the healthy development of international education cooperation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".