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Record W2795950919 · doi:10.1163/22125868-12340082

Beyond the Cultural Approach

2017· article· en· W2795950919 on OpenAlexaffabout
Jingzhou Liu

Bibliographic record

VenueInternational Journal of Chinese Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFriendshipInternationalizationCultural diversityAffect (linguistics)Gender studiesSociologyStudy abroadEthnic groupPsychologyPedagogySocial science

Abstract

fetched live from OpenAlex

Chinese international students are vital to internationalization development in Canadian higher education, providing immediate and significant social and economic benefits to Canadian society. The existing scholarly studies have primarily adopted a cultural approach, with a focus on intercultural adaptation or related cross-cultural perspectives. This study goes beyond the cultural approach and examines how race, gender, and class intersect in producing social inequality among Chinese international students in Canada. Through the narratives of five students attending higher education institutions in British Columbia, the study reveals that Chinese international students have experienced discrimination in relation to developing friendship, integrating to the learning environment, and accessing supports and resources on campus based on the color of skin, their gender, and misperception of their class. The color line divides them into the “dominant white” and “people of color.” Color blindness negates their racial identities and ignores the ways in which these affect their learning experiences. The findings of this research call for an intersectional approach to examine international students and their lived experiences by addressing students’ multiple identities and differences to enrich their lived experience in Canada.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.029
GPT teacher head0.412
Teacher spread0.383 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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