The internationalization of Chinese scholarly journals based on publications deriving from the G8 countries
Bibliographic record
Abstract
China’s rapid rise in international collaboration and scientific research has been widely documented, but the progress of Chinese scholarly journals towards internationalization has been less investigated. This study examines the internationality of Chinese scholarly journals using bibliometric analysis of publications deriving from the eight highly industrialized countries (the Group of 8, or G8) namely, Canada, France, Germany, Italy, Japan, Russia, the UK and the USA, from 1979 to 2016 based on the databases of the Chinese National Knowledge Infrastructure (CNKI). Annual production and research trends, research affiliations, research emphases and foci, and common journal sources were analyzed. The analysis reveals that the internationality of Chinese scholarly journals has been continuously growing since 1979 and increased rapidly from 2004 to 2010. Both foreign researchers’ submissions and internationally co-authored Chinese publications substantially contributed to the internationalization of Chinese scholarly journals. This internationalization was also influenced by the academic scopes, journal titles and disciplinary categories of Chinese journals. The potential implications and limitations of this study are discussed
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".