International students in China: regional distribution and macro-influencing factors
Bibliographic record
Abstract
As a key player in intellectual migration, international students are affected by various micro-, meso- and macro-level factors when making their study destination choice. Existing literature on this topic mostly adopts a qualitative approach and limits to investigations of country choice. By applying exploratory spatial data analysis and spatial econometric modelling on a set of panel data, this study instead focuses on macro-level analyses of international students’ regional destination choice in China. First, we found that the spatial distribution of international students in China have changed over the course of our 1999–2018 study period. International students primarily concentrate in and/or around economic hubs or intellectual gateways although increase in semi-intellectual gateways are also observed. Second, international students in China has spatial effects and their study area choice is significantly affected by the number of international students studying there in the past, the quality of higher education, the availability of public infrastructure, touristic attractiveness, and the presence of policy incentives. These factors exercise greater influence on degree-seeking than non-degree-seeking students. Together, they represent persistence effect, learning and living environment effect, and spatial diffusion effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".