FACTORS THAT CONTRIBUTE TO CULTURE SHOCK: A CASE STUDY INVOLVING CHINESE LEARNERS OF ENGLISH
Bibliographic record
Abstract
Given the reality of large numbers of Chinese students studying in colleges and universities in Canada to pursue higher education, and the alienation that they experience during their studies, it is important that educators understand the nature of culture shock, the factors that contribute to culture shock, as well as the role that culture shock plays in the process of cross-cultural adaptation. The purpose of this study is to investigate the relationship between each of the six demographic factors (e.g. age, gender, future study plan, number of Canadian friends who are native speakers of English, previous travel experiences and length of stay in Canada) and culture shock linguistically, socially, and psychologically. Two instruments were developed to collect data for this research. First of all, the researcher developed a questionnaire and translated it into Chinese to ensure that students fully understood the content. Fifty-five Chinese-speaking international students who were enrolled in two different English language institutes in Ontario, Canada responded to the survey questionnaire for this study. Secondly, personal interviews were employed. Of the students who indicated willingness to participate in the in-depth interview, four, one male and one female from each of the two language institutes, were selected as the informants. The interviews were tape recorded and then transcribed by the researcher for further analysis. Data collected from the questionnaire were analyzed by using Excel and SPSS. Qualitative methods were applied to analyze the data collected from the personal interviews. iii The results indicated that demographic factors such as age, gender and previous travel experiences were not variables that influenced culture shock adjustment of Chinese English learners. However, the number of Canadian friends who are native speakers of English, plans for future study and students’ length of stay in Canada were variables associated with lower culture shock linguistically, socially and psychologically. Based on the data collected from the personal interviews, problems such as English language challenges, racial discrimination problems, financial difficulties, emotional problems and academic concerns were also identified. Meanwhile, students also expressed their satisfaction with Canada’s respect for individuality and diversity, the politeness of Canadian people and the convenience of life in Canada.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".