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Record W1690412603 · doi:10.47678/cjhe.v36i2.183540

Attracting East Asian Students to Canadian Graduate Schools

2006· article· en· W1690412603 on OpenAlexaffvenueabout
Liang‐Hsuan Chen

Bibliographic record

VenueCanadian Journal of Higher Education · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersOffice of International Science and Engineering
KeywordsChinaInternationalizationGovernment (linguistics)Graduate studentsGraduate educationHigher educationPolitical scienceImmigrationEconomic growthPublic relationsSociologyPedagogyBusinessEconomics

Abstract

fetched live from OpenAlex

This study seeks to identify factors influencing East Asian international students’ choices of Canadian graduate schools, to assess the strengths and dynamics of the factors influencing enrolment decisions, and to describe possible implications both for the Canadian government and for Canadian universities offering graduate education. The research sample comprised 140 students from China, Hong Kong, Japan, Korea, and Taiwan who enrolled in graduate programs at two large Ontario universities. The research findings reveal the significant influence of academic, economic, environmental, and visa/immigration pulling factors as well as a set of negative pushing factors from third countries such as the United States. Activities related to internationalization of graduate education play a critical role in influencing the choice of a Canadian graduate school. The findings suggest that to attract the “best and brightest” international graduate students, policy makers and institutional administrators should focus on investing in research and ensuring the quality of graduate education while devoting efforts and resources to the internationalization of Canadian graduate 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.034
GPT teacher head0.345
Teacher spread0.311 · 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 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

Citations36
Published2006
Admission routes3
Has abstractyes

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