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Record W4376275480 · doi:10.5539/jel.v12n3p135

Internationalization at Home: A Sustainable Model for Chinese Higher Education in the Post-pandemic Era

2023· article· en· W4376275480 on OpenAlexvenueno aff
Feng Guo

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationChinaCurriculumPandemicPolitical scienceInternational educationEconomic growthSustainable developmentHigher educationStudy abroadCurriculum developmentInternationalization of Higher EducationPublic relationsSociologyCoronavirus disease 2019 (COVID-19)PedagogyBusinessEconomicsMedicineInternational tradeLaw

Abstract

fetched live from OpenAlex

Internationalization at Home (IaH) has become a hot research topic although it is not a new concept appeared recently especially in China. The concept of IaH has experienced several changes since it was created in 1990s while the widely used one refers to the purposeful integration of international and intercultural dimensions into the formal and informal curriculum for all students within domestic learning environments. The recent development of the concept pays more attention on the establishment of international communities for each individual included. China changed its strict pandemic prevention and control policy in the end of 2022 and moved forward into the post-pandemic era. The practice of IaH has already appeared in China since the reform and opening-up policy carried out in 1978. However, the focus of Chinese policy and practice changed from international mobility to Internationalization at Home with a long-term development. The pandemic fastens this changing process. Due to the theoretical and practical background, IaH is recognized as the sustainable mode for Chinese higher education in the post-pandemic era which represents the future development path.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.502

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 studies0.0000.000
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 head0.017
GPT teacher head0.364
Teacher spread0.347 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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