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Record W4402278482 · doi:10.14425/jice.2024.13.2.0821

China as a Destination for International Students: A “Pull and Repel” Factors Analysis in the Post-COVID-19 Era

2024· article· en· W4402278482 on OpenAlex
Wei Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of International Comparative Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChinaCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPolitical scienceGeographyVirologyMedicineInternal medicineInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

International student mobility is a complex phenomenon influenced by numerous factors.This study examines the prospect of China as a destination country for international students in the post-COVID-19 era.With qualitative data from 30 frontline international educators (support staff in international student recruitment and services) from 30 Chinese universities, this study has determined a set of "pull" factors that serve to attract international students to study in China and a set of "repel" factors that discourage students from going.On the basis of both the "pull" and "repel" factors identified, the participants anticipate important challenges for China's international enrollment in the short term, but stay optimistic about the long-term prospect.The "pull and repel" factors analysis is found to be a useful approach to examining the attractiveness of a host country to international students in a focused manner.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.064
GPT teacher head0.478
Teacher spread0.415 · 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