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Record W3201111863 · doi:10.3390/su131810378

Chinese Universities’ Cross-Border Research Collaboration in the Social Sciences and Its Impact

2021· article· en· W3201111863 on OpenAlexaboutno aff
Yang Liu, Jinyuan Ma, Huanyu Song, Ziniu Qian, Lin Xiao

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsScopusInstitutionPolitical scienceQuality (philosophy)BibliometricsLibrary scienceSocial scienceSociologyMEDLINELaw

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gptMetaresearchBibliometrics
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.071
metaresearch head score (Gemma)0.102
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0710.102
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0130.274
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0010.001
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 head0.326
GPT teacher head0.723
Teacher spread0.397 · 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

Labeled directly by 3 models reading the full record.

MetaresearchBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
DomainMethods
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

Citations19
Published2021
Admission routes1
Has abstractyes

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