Understanding the Impact of WeChat Use on Chinese International Students’ Social and Cultural Capital in Australia
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».