Applying clustering coefficient to the pattern of international authorcollaboration in the topic of addiction and clinical research
Bibliographic record
Abstract
OBJECTIVE: To apply cluster coefficient (CC) to the pattern of international author collaborations on the topic of addiction and clinical research using data from Medline and to visualize the results using Google maps and social network analysis (SNA). METHOD: We obtained 647 abstracts on December 22, 2017, from Medline based on the keywords of addiction and clinical research since 1989. The author names, countries, and keywords were recorded. We also made a note of the following features: (1) nation distribution for 1st author’s and most popular journals; (2) eminent authors in addiction and clinical research, (3) notable keywords representing addiction and clinical research, and (4) cluster coefficients similar or different between author and keyword networks. We programmed Microsoft Excel VBA routines to extract data from Medline. Google Maps and SNA Pajek software were performed to display the graphical representations with an easy-to-read feature for readers. RESULTS: We found that (1) the most number of papers on the topic of addiction and clinical research are from the U.S. (225, 37.19%) and Australia (86,14.21%); (2) the productive authors with the highest cluster coefficient in addiction and clinical research are Abe, Yoshinari(Canada) and Agid, Ofer(Japan); (3) the most linked keywords are clinical research and drug addiction; (4) both author and keyword networks present higher CC in their networks. CONCLUSION: Social network analysis provides wide and deep insight into the relationships among nations and co-authors. The results can provide readers with knowledge and concept diagram for future submission to a journal in addiction and clinical research.
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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 | 0.013 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".