The Roots of the Challenge: Undergraduate Chinese Students Adjusting to American College Life
Bibliographic record
Abstract
Recent economic development in China not only has improved the overall living standards of Chinese people, but it has also created a new middle class. Another impact of the economic development is the increasing demand for educated workers. Subsequently, the demand for quality higher education has also increased. With more than 50% of the world’s top 100 universities located in the United States, the United States is regarded as the number one destination for international students for higher education. Due to the cultural differences between China and the United States, scholars have found that Chinese students encounter the most challenges adjusting to American college life. Lack of Western cultural exposure, different cultural values, the effect of the One-Child Policy, the emphasis on effort, endurance, and hard work in education, the continual impact of the Cultural Revolution’s aftermath on people’s relationships, the unfulfilled expectations of American college life experiences, and the influence of the Chinese education structure on students’ characters and skills-building all have an impact on Chinese students’ worldview and their interaction with their new environment. By reviewing the literature on the topic, this article seeks to understand the roots of the challenge to gain insight into the reasons why Chinese students do what they do after they come to the United States for higher 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".