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Record W2915070805

Towards Equality in Age of Internationalisation of Higher Education: Revisiting Chinese International Students

2018· article· en· W2915070805 on OpenAlexaffabout
Jingzhou Liu

Bibliographic record

Venue2018 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIntersectionalityInternationalizationInternational educationGender studiesSociologyHigher educationClass (philosophy)Cultural capitalSocial classNarrativePolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

Chinese international students are historically vital to internationalization strategies in Canadian higher education, providing immediate and significant economic benefits to Canadians. At the same time, they face many challenges. Hence, it is imperative to understand Chinese international student experiences in Canada. Existing studies primarily adopted a cultural approach, with a focus on cultural shock and differences, and neglected social inequalities related to race, class, and gender. This study intends to go beyond the cultural approach to examine how race, gender, and class are intersected in producing social inequality among Chinese international students in Canada. Through the narratives from six students attending three different institutions in British Columbia, research findings reveal that the intersection of race, gender, and class shaped student lived experiences in learning and class engagement, making friends, entering the Canadian labour market, and accessing social resources. Thus, this research challenges the deficit model applied to international students and calls for an intersectionality approach to examine international students and their lived experiences in Canada by understanding students

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.683

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.415
Teacher spread0.329 · 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 designObservational
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

Citations0
Published2018
Admission routes2
Has abstractyes

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