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Record W3203962048 · doi:10.5539/ies.v14n10p95

Study on Internationalization Strategy of China’s New Business Education in the Background of Digital Economy

2021· article· en· W3203962048 on OpenAlexvenueno aff
Jia Lin Xie, Tianshuo Zhang

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationContext (archaeology)Digital economyConnotationInternational businessHigher educationChinaBusiness educationKnowledge economyDigital transformationBusiness modelBusinessMarketingPublic relationsEconomic growthEconomicsEconomyPolitical scienceManagementInternational trade

Abstract

fetched live from OpenAlex

As the global economy is undergoing transformation and upgrading in the background of the digital economy, it leads to a reformation of business education in the new context, which brings the concept of new business education in China. One of the significant features of the new business education is multidisciplinary teaching and learning. Meanwhile, it is closely related to strategic decision-making in disciplinary program design, research design, faculty recruitment, teaching models, and international strategies. Benefited from the internationalization of higher education in past years, traditional business education has gained an advantage in introducing international students, teachers, and resources and building global cooperation platforms, including international visits and multiple studies in an international context. However, it cannot meet the demand for cultivating talents in the era of the digital economy. This paper starts with the connotation and feature of new business education, discussing internationalization strategy with a fresh perspective and unique positioning. Meanwhile, it aims to provide a theoretical and practical value for China’s business schools with internationalization strategy making in the background of the digital economy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

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 head0.082
GPT teacher head0.435
Teacher spread0.353 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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