Chinese Research Trends in Materials Science and the Korean Countermeasure
Bibliographic record
Abstract
Recent rapid development of the Chinese economy based on science and technology is challenging Korean industries and economy.Since the driving force for this rapid development of China is known to be scientific technologies, the purpose of this research is to confirm the current status of Chinese scientific research in the field of "Materials Science and Engineering" and propose a strategy for competition with China.Even though there are numerous journals of "Materials Science and Engineering", the 10 most popular journals with high impact factors were selected to cover general materials, nano materials, bio materials, and electronic materials.It was found that the number of scientific papers written by Chinese scientists for the materials field in the 10 journals was slowly increasing from the year 2000 until 2005, but has been rapidly increasing since 2005.This research found that Chinese research activities in the traditional metallic materials and nano materials have tremendously increased to occupy around 30 % or more papers published in several major journals related with materials science and engineering.On the other hand, bio materials and electronic materials research has not been pursued so actively; however, very recently the number of publications in these fields is also beginning to increase.To compete with this tremendously growing Chinese scientific development, Korea should have a policy of "selection and concentration" in materials-related fields, including basic science in nano, bio, and electronic materials.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".