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Record W3046496181 · doi:10.5539/ass.v16n8p131

Research on the Current Status and Policy Evolution of International Education Industry in China

2020· article· en· W3046496181 on OpenAlexvenueno aff
Ye Yankun

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
FundersSichuan Agricultural University
KeywordsChinaInternational educationScale (ratio)International relationsPoliticsPolitical scienceInternational marketEconomic growthHigher educationEconomicsInternational tradeGeography

Abstract

fetched live from OpenAlex

The paper analyzed the current status and policy evolution of international education industry in China based on statistical data of the education market scale, source countries, education levels and study subjects. It is found that the international education in China has undergone three major stages, including the stage of “unified national management dominated by politics”, the stage of “exploration of the model for self-financed study abroad”, and the stage of “all-round open development”. The achievements of international education industry are closely related to the key policies in the 3 stages. The research shows that the scale of international education in China is basically the same as that in the developed countries but there exists a big import and export deficit since China has slightly smaller international market share. Asian and African countries are the main sending countries of international students, and students from these countries prefer to receive academic education in China than those from Europe and America. In terms of major subjects, Chinese language and literature are still the main subjects, but engineering, economics and management are becoming more popular. Finally, suggestions are made on how to further expand the international education market and optimize the international education structure in China.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.060
GPT teacher head0.459
Teacher spread0.399 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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