The citation analysis of Chinese Journal of Orthopaedics from 2005 to 2009
Bibliographic record
Abstract
Objective To evaluate the academic level and the popularity of Chinese Journal of Orthopaedics from the point view of citation. Methods According to the information of Chinese Medical Citation Index (CMCI), the amount and distribution of the original articles in Chinese Journal of Orthopaedics cited by the journals included by CMCI were statistically analyzed. The data analysis included the percentage of cited articles, the number of citation of a single article, authors of most cited articles, geological regions of cited articles, citing journals, the self citing rate and years of citation. Results The percentage of cited papers (663 articles, 3728 times) in all published 1 182 articles in Chinese Journal of Orthopaedics from 2005 to 2009 was an average of 56.09%. The average time of original articles cited by other researchers was 5.62. The highest one was citated by 104 times. 210 articles (31.68% of total papers) were cited 5 or more times, and the all cited times from them were 2835 (76.04% of total cited times). 663 cited articles were written by 485 authors from different countries. The most frequently cited author wrote 12 papers. The number of authors with one paper cited was 386 (79.58% of total authors). The cited authors are from the mainland, HongKong, and Taiwan in China, as well as USA, Canada, France. Beijing, Shanghai and Tianjin are leading cities in orthopaedic research. Beijing was the first place (32.16% of total authors). There were 445 citing journals. There was 217 items self cited; the self- citing rate is 0.058. Conclusion Chinese Journal of Orthopaedics has provided high quality articles and has a strong influence in the field of medical research. It has been become an important resource for orthopaedic researchers and a vital medical journal in China. Key words: Periodicals; Bibliometrics; Statistics
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".