Patterns of Iran's research collaboration in the field of pharmacology and pharmacy: A bibliometric study
Bibliographic record
Abstract
Purpose: This research aims to analyze and visualize the structure of Iranian scholarly networks in the field of “pharmacology and pharmacy”. This study includes an overview of co-authorship, efficiency and ranking of the researches, visualizing the co-authorship network, changes in the main core of the publications and macro and micro-level metrics such as social influence. Methods: This research utilizes social network analysis (SNA). The preliminary data of this research includes all the Iran’s documents in Web of Science in “Pharmacology and Pharmacy” during the period of 2005 to 2016. After the preprocessing of 6204 records and creating relational matrix, a combination of bibliometric software (including UCINET, NetDraw, HistCite and VOSviewer) were used to analyze and uncover network features. Results: Results indicated that most papers are multi-authored. Four-authored articles are the main common authorship pattern. Some measures such as author frequency, multi-authored papers, and single-authored papers in each time interval are ascending. Moreover, “density” reduction of the scientific collaborations indicates that fragmentation level has increased based on the “clustering coefficient” in each period. Besides, Iranian researchers of the field has the most collaboration with the scholars of England (%2.85), U.S.A. (2.61%) and Canada (1.76%), respectively. Conclusions: Fragile structure and low closeness of the network imply low maturity of Iran’s research in the field of “pharmacology and pharmacy”. Also, test of the correlation coefficients indicates that with increasing “degree centrality” and “betweenness centrality”, the “number of articles” increases as well. However, there is no correlation between “closeness centrality” and “number of articles”.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".