Understanding the Impact of WeChat Use on Chinese International Students’ Social and Cultural Capital in Australia
Bibliographic record
Abstract
In this study we explore how Chinese international students’ (CISs) use of WeChat influences their ability to acquire and use different forms of capital – particularly cultural and social capital – while studying at an Australian higher education institution. Drawing on Bourdieu’s theory of practice, we examine how these forms of capital shape and are shaped by students’ experiences. We drew key findings from semistructured interviews and the scroll-back method with 15 CISs. We made three findings. First, WeChat helps many CISs overcome language barriers, which are an immediate and prominent marker of the cultural capital they often lack but need to navigate the Australian higher education field. Second, WeChat supports the development of social capital by enabling CISs to maintain and form new social networks. Third, while CISs show agency in identifying and acquiring capital, the ongoing interaction between field (the university context) and habitus (their internalised dispositions) also shapes their success. The findings underscore the importance of helping CISs position themselves to build relevant forms of capital as they adapt to Australian academic life. The study offers recommendations for higher education decision-makers on how to support CISs’ WeChat use, inform policymaking and strengthen the university’s reputation both nationally and globally.In this study we explore[CRS1] how Chinese international students’ (CISs) use of WeChat influences their ability to acquire and use different forms of capital –[CRS2] particularly cultural and social capital – while studying at an Australian higher education institution. Drawing on Bourdieu’s theory of practice, we examine[CRS3] how these forms of capital shape and are shaped by students’ experiences. We drew[CRS4] key findings from semistructured[CRS5] interviews and the scroll-back method with 15 CISs. We made three[CRS6] findings. First[CRS7] , WeChat helps many CISs overcome language barriers, which are an immediate and prominent marker of the cultural capital they often lack but need to navigate the Australian higher education field. Second, WeChat supports the development of social capital by enabling CISs to maintain and form new social networks. Third, while CISs show agency in identifying and acquiring capital, the ongoing interaction between field (the university context) and habitus (their internalis[CRS8] ed dispositions) also shapes their success[CRS9] . The findings underscore the importance of helping CISs position themselves to build relevant forms of capital as they adapt to Australian academic life. The study offers recommendations for higher education decision-makers on how to support CISs’ WeChat use, inform policymaking[CRS10] and strengthen the university’s reputation both nationally and globally. [CRS1]Revised for anthropomorphism, the attribution of human traits or intentions to a nonhuman entity. The study cannot explore. [CRS2]Mindful of your instruction not to change the format and layout, punctuation is not formatting. In your selected UK English, use open en dashes not closed em dashes to break text. [CRS3] [CRS3]Revised for anthropomorphism. The study cannot examine. [CRS4]Revised for APA’s preferred active voice wherever possible. [CRS5]One word in APA. See https://dictionary.apa.org/semistructured-interview [CRS6]Revised for better structure. [CRS7]No –ly ending in ordinals. [CRS8]Revised for UK spelling. [CRS9]Revised for active voice. [CRS10]UK English does not use the Oxford comma, a comma before the conjunction in a list of three or more items.
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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 | 0.002 | 0.007 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".