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Record W4309084546 · doi:10.5206/cieeci.v51i1.14220

Challenges of South Asian and Chinese International Students: Becoming Anchored in Networks of Support

2022· article· en· W4309084546 on OpenAlexaffvenueabout
Nancy Mandell, Janice Phonepraseuth, Jana Borras, Larry Lam

Bibliographic record

VenueComparative and International Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsFutures contractPublic relationsWork (physics)International educationSocial network (sociolinguistics)SociologyChinaPolitical scienceHigher educationBusinessSocial mediaEngineering

Abstract

fetched live from OpenAlex

When coming to Canada to pursue post-secondary education, international students experience academic, financial, employment, and social and emotional challenges. Drawing on social network studies and studies of social anchoring, twenty-three in-depth interviews with South Asian and Chinese international students reveal the ways they navigate these issues in their post-secondary school environments. We find that international students work hard to establish social networks by making new connections at school, and students turn to their institutional networks for assistance. However, institutional networks alone are inadequate, and international students must also find support and advice through their family and community networks. We conclude that having strong institutional, family, and community networks are crucial to the social anchoring of international students in Canadian society. Social networks mitigate the challenges that international students encounter as they build their futures through post-secondary education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.326
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.008
Scholarly communication0.0080.003
Open science0.0010.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.090
GPT teacher head0.432
Teacher spread0.342 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2022
Admission routes3
Has abstractyes

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