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Record W4411742341 · doi:10.47116/apjcri.2025.06.23

An Analysis of the Current Status in Attracting International Students in Korea and Abroad

2025· article· en· W4411742341 on OpenAlexaboutno aff

Bibliographic record

VenueAsia-pacific Journal of Convergent Research Interchange · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent (fluid)Study abroadPolitical scienceBusinessEngineeringElectrical engineeringLaw

Abstract

fetched live from OpenAlex

A studyaims to analyze the current status of international students in Korea from multiple perspectives in the context of globalized education, while also examining the trends and challenges regarding international students in other countries.By analyzing the status of international students both domestically and in major countries, this study seeks to propose strategies for maintaining and developing Korea's policies on international students in order to compete with educationally advanced nations.To achieve this, the study analyzes the status of international students in 2024, including factors such as degree programs, academic disciplines, the number of students per 10,000 domestic students, university recruitment, regional distribution, and countries of origin.Additionally, the study examines the status of international students in major countries, including the United States, the United Kingdom, Canada, China, and Japan, which are considered leaders in international education.Based on this analysis, the study suggests future tasks for improving the policies.These tasks include diversifying the recruitment of international students, improving management systems, and strengthening employment linkage programs.The recruitment policy for international students is not only related to higher education but is also closely connected to broader societal issues in Korea.Therefore, it is a crucial topic that requires comprehensive and multifaceted discussions moving forward.

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)0.003
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 score0.022
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.452
Teacher spread0.382 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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