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Record W3176396580 · doi:10.1080/13603116.2021.1941318

Chinese international graduate students at Canadian universities: language barriers, cultural identities and perceived problems of engagement

2021· article· en· W3176396580 on OpenAlexaffabout
Meng Xiao

Bibliographic record

VenueInternational Journal of Inclusive Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsDisadvantagedStudent engagementPedagogyInterviewSociologyCultural diversityDiversity (politics)Medical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This study investigates the socio-cultural experiences of Chinese international graduate students in a Canadian university. Specifically, this research explores the multiple challenges of their engagement in and out of the classroom as it relates to their language and culture. Qualitative data was collected by interviewing students and staff at the university. The findings show that Chinese international graduate students’ experiences as passive learners and reduced engagement were disadvantaged in Canadian university classrooms. This is because active engagement is preferred in the Western-dominant ideology of student engagement in Canadian graduate schools. Implications from this study suggest that faculty and staff should deconstruct the Western dominant ways of the understanding of student engagement by empowering inclusivity and diversity of multiple languages and cultural identities. The study also provides some practical suggestions for the Chinese international graduate student population to better engage in Canadian graduate institutions and for educators and practitioners to better support Chinese international students in Canadian post-secondary schools.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.383
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2021
Admission routes2
Has abstractyes

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