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The Impacts of Higher Education Globalization on Chinese Universities

2012· article· en· W1846982498 on OpenAlexvenueno aff
HU Chang-ying

Bibliographic record

VenueHigher education of social science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationHigher educationGovernment (linguistics)Developing countryQuality (philosophy)Economic growthPolitical scienceDeveloped countryKnowledge economyDevelopment economicsEconomicsSociology

Abstract

fetched live from OpenAlex

Globalization has increased the competitions of knowledge and economy between countries, and these competitions in turn made higher education become an important tool of enhancing the competitiveness of every country. However, today, the core thought pattern of global higher education is dominated by the western developed countries, which has affected the development of higher education in the developing countries seriously, and many developing countries just accept the higher education mode of the developed countries passively, but cannot affect the world due to their limited science & technology and economy level. Therefore, the development of higher education in many developing countries is confused and disoriented. In this paper, we will discuss the impacts as well as the plights and confusions that globalization of higher education brought to Chinese universities, and at the same time, discuss the measures that Chinese government and universities did in these years to adapt to the globalization. Finally, we will give some suggestions to improve the quality and efficiency of Chinese universities. Key words: Impacts of globalization; Higher education; Plights and confusions; Reform measures

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)0.003
metaresearch head score (Gemma)0.004
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.350
Teacher spread0.338 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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