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Record W4409920409 · doi:10.63650/jeve.v3i2.42

The Development Status of Sino-Foreign Cooperative Education Programs in Higher Vocational Education Based on Quantitative Data Analysis

2025· article· en· W4409920409 on OpenAlexaboutno aff
H.Y. Chen C.J. Cheng

Bibliographic record

VenueJournal of Exploration of Vocational Education · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationMathematics educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

On the basis of the program list released by the Ministry of Education in China, there are 893 Sino-foreign cooperative programs in higher vocational education nationwide, with a total enrollment scale of 67,715 students. The regional distribution displays a decrease from the eastern area to the western area on educational programs and enrollment scale. A total of 228 majors are enrolled in the program, among which Accounting is the most popular, followed by Computer Application Technology. The convergence of specialty settings is serious. Jiangsu has the most foreign-partner universities, followed by Zhejiang and Shandong. In the same province, municipality and autonomous region, the number of foreign-partner universities is generally more than that of Chinese partner universities. Foreign partner universities are mainly from Australia, Canada, the United Kingdom and the United States, mainly in developed countries. The development scale of Sino-foreign cooperative programs is closely connected with the development and demand of regional economy. In terms of policy, Sino-foreign cooperative programs in western China and in remote and poor areas should be encouraged. China can combine its own professional advantages with foreign high-quality educational resources to set up characteristic cooperative programs.

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 score0.836
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.127
GPT teacher head0.377
Teacher spread0.250 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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