CiteSpace-based Survey of International Advances in Chinese Traditional Culture Research in 1999-2019
Bibliographic record
Abstract
With the globalization strategy and the initiative “One Belt and One Road” in China, Chinese traditional culture has a great impact on the world. This study, based on CiteSpace, explores the total numbers, hotspots, characteristics and tendency of international research on Chinese traditional culture, by retrieving 866 articles from SSCI and A&HCI in the Web of Science (Core Collection). The results of the study revealed that: there is a strong increases in total output from 1999 to 2019 and the research on the theme of Chinese traditional culture has become interdisciplinary; the most productive authors aren’t the ones in the most cited documents and the journals on which the most cited documents publish aren’t the prolific journals; most of the researchers are from Hong Kong, Taiwan, mainland of China, Canada and USA. In view of the results, it is obvious that it is a good way to spread Chinese traditional culture over the world. It is strongly recommended that we should do more analysis on the international literatures with different paradigms, methods and metrics in order to get a more comprehensive and correct domain visualization map of Chinese traditional culture research.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.083 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".