The Disruptive Innovation Evaluation and Empirical Analysis of Chinese, Japanese, Indian, and South Korean Scientific Journals
Bibliographic record
Abstract
This study first used the Journal Integrated Disruption Index (JIDI) to evaluate the disruptive innovation level of scientific journals published by China, Japan, India, and South Korea from 2010 to 2019. Then the authors analysed the annual trend and correlation of academic influence and disruptive innovation level of these journals. Finally, the selected journals were compared with Nature and Science annually to better measure their development trend and provide a reference for their further development. The study found that the average disruptive innovation level of journals from three countries except Japan has been rising. Selected journals’ academic influence, disruptive innovation level, and number of research articles were significantly correlated, but the correlation coefficients differed between the different attributes. At present, the disruptive innovation level of authoritative journals of four countries is still far from Nature and Science and the gap of disruptive innovation level is far greater than that of academic influence. Although the academic influence and the disruptive innovation level of selected journals are rising, the attractiveness to disruptive findings needs to be further improved. Therefore, Asian countries should implement scientific evaluation and journal evaluation systems that encourage innovation and promote academic research under the guidance of innovation.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".