Chinese Universities’ Cross-Border Research Collaboration in the Social Sciences and Its Impact
Bibliographic record
Abstract
This paper examined the coauthorship patterns in Chinese researchers’ cross-border research collaboration in the social sciences based on articles and reviews indexed in the Scopus database (2010–2019). We explored the evolution of coauthorship patterns by proportion of collaboration, year, research field, country/region, and research institution; additionally, the quality/impact of the coauthored publications was examined using four levels of paper quality (Q1–4), citations per paper, and FWCI. We found that collaboration between Chinese and international scholars is very common, and more than 40% of all papers published by Chinese scholars from 2010 to 2019 involved cross-border collaboration. The growth in collaboration was very steady over the past 10 years, increasing by an average of 20% per year. United States scholars are the most common research collaboration partners for Chinese scholars in the social sciences, followed by those in Hong Kong, the United Kingdom, Australia, and Canada. The field of psychology seeks the most collaboration, followed by economics and finance, business and management, and social issues. The percentage of Q1 papers increased from 36% in 2010 to 66% in 2019. Thus, in the past 10 years, Chinese scholars’ cross-border collaboration has grown extensively in terms of both quantity and impact.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchBibliometrics Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.071 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.274 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".