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Record W2016634615 · doi:10.5539/ies.v8n3p55

Goals, Strategies, and Achievements in the Internationalization of Higher Education in Japan and Taiwan

2015· article· en· W2016634615 on OpenAlexvenueno aff
Hsuan-fu Ho, Ming-Huang Lin, Cheng‐Cheng Yang

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationInternationalization of Higher EducationHigher educationBusinessInternational educationEconomic growthPolitical sciencePublic relationsMarketingEconomicsInternational trade

Abstract

fetched live from OpenAlex

International knowledge and skills are essential for success in today’s highly competitive global marketplace. As one of the key providers of such knowledge and skills, universities have become a key focus of the internationalization strategies of governments throughout the world. While the internationalization of higher education clearly has certain benefits for students, schools, the national economy, and the international community, each country gives a different degree of importance to each of these various benefits. The purpose of this study was threefold: 1) to determine which benefits of the internationalization of education are deemed most important in Taiwan and Japan; 2) to determine which measures are most effective for realizing these benefits; and 3) to determine the extent to which these measures have actually been carried out. A questionnaire was used to obtain the views of 100 professors, 50 in Taiwan and 50 in Japan, as to the current situation in their respective countries. The results indicate that there are significant differences between the two country’s reasons for promoting the internationalization of education, as well as in their respective internationalization strategies.

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)0.000
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 score0.299
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.069
GPT teacher head0.434
Teacher spread0.364 · 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

Citations21
Published2015
Admission routes1
Has abstractyes

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