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Record W2922301774 · doi:10.3102/1680118

A Comparative Study of Internationalization Policies in Chinese and Canadian Higher Education

2021· article· en· W2922301774 on OpenAlexaffabout
Yu Shuai

Bibliographic record

VenueProceedings of the 2021 AERA Annual Meeting · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInternationalizationChinaInternationalization of Higher EducationHigher educationPolitical scienceCorporate governanceEducation policyKnightEconomic growthPublic administrationBusinessInternational tradeEconomics

Abstract

fetched live from OpenAlex

Although internationalization plays an increasingly important role in higher education, it has been perceived as both innovative and turbulent in the past several decades (Knight, 2008). This study explores and compares the contexts and challenges of internationalization of Chinese and Canadian higher education. By applying the “Global Higher Education Matrix” created by Jones (2008) and the “Four Approaches to Internationalization of Higher Education” (Knight, 1997; Zha, 2003) as an analytical framework, this study analyzes the different priorities of the internationalization policies implemented by global organizations, national/ federal governments, provincial governments, and institutions in Canada and China. \nUsing a comparative policy approach, this study informs the policy and practice in the internationalization of higher education in both China and Canada. Due to the different internationalization contexts, the internationalization policies of Chinese and Canadian higher education promote different priorities on five aspects: academic mobility, cooperation and partnership, internationalizing curriculum, quality assurance, and administration and governance.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.327
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 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
Published2021
Admission routes2
Has abstractyes

Explore more

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