A Survey on Co-authorship Network of Iranian Researchers in the field of Pharmacy and Pharmacology in Web of Science during 2000-2012
Bibliographic record
Abstract
Introduction: Co-authorship network is a kind of social network that presents significant information about collaboration among authors, which is one of the most important factors of the qualitative and quantitative growth of the scientific publication. Regarding the fact that during 2000-2012, Pharmacy and Pharmacology has had the most number of publications in WoS among institutions belonging to the Ministry of Health and Medical Education (MHME), in this research, the co-authorship network of this field is investigated and analyzed. The results of this study can clarify the dimensions of collaboration in the field and help research policy as well.\nMethods: The present study was conducted through scientometric method and social network analysis. The data were drawn from the WoS, downloaded in June 2013, and analyzed by PAJEK software. The research population included 3514 Pharmacy and Pharmacology documents published by universities affiliated to the MHME during 2012-2000. \nResult: The results of the study showed that the majority of collaborations of the Iranian researchers have been from England, USA and Canada, respectively. Co-authorship network of researchers consisted of 90 nodes (authors) and density degree of the network is 0.084. There is a strong linear correlation between the number of publications and degree centrality of authors at the 1 % level. \nConclusion: The degree density of the network under study (.084) shows that this network has a low density. In fact, authors (nodes) in the network have had few relations with each other. This research also revealed that researchers with more publications are likely to have more collaborative works.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 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".