The Multiple Forces Behind Chinese Students' Self-segregation and How We May Counter Them
Bibliographic record
Abstract
With the internationalization of Higher Education in Canada, universities have been striving to provide a welcoming and inclusive environment for international students.However, sometimes their efforts fall short due to a lack of deep understanding of the international student body.This study focuses on one particular international student group -students from mainland China -and aims to uncover some of the crucial reasons behind the widely reported self-segregation of Chinese students (Cheng & Erben, 2011).It sets to understand why many students from mainland China feel offended and turned off by cross-national communications with students from the host nation (Dewan, 2008).I employed various frameworks to understand the findings from the study, including host nation hostipitality, social psychology and group identity, and the impact of colonial mentality and Chinese nationalism.The goal of the study is to shed light on strategies educators may employ to help mitigate the self-segregation pattern among Chinese international students and encourage more inclusive learning environments and communities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".