Mapping and Analyzing the Co-authorship and Thematic Networks of Kurdistan University of Medical Sciences in the Science Citation Database from 2011-2016
Bibliographic record
Abstract
Abstract Background: Considering the importance of co-authorship and thematic networks in expanding specialization and improving the quality of scientific works, the purpose of this research is to map and analyze the co-authorship and thematic networks of the Kurdistan University of Medical Sciences in the Science Citation Database between years 2011-2016. Method: Research society of scientific publications in Kurdistan University of Medical Sciences, indexed in the science citation database between years 2011-2016. In order to analyze the data, Excel and HistCite software, and to map the networks and analyze them the Citespace and Gephi software were used. Findings: The number of scientific publications of Kurdistan University of Medical Sciences during the studied years is 518 papers with 4914 citations. The average number of citations for each paper was 9 and the dominant co-authorship pattern in these years is a five-author pattern. The collaboration index for the years under examination is 5.66, the degree of collaboration is 0.99 and the collaboration coefficient is 0.98. The researchers' desire to create a co-authorship network between years 2011-2016 has increased and the largest international collaboration of university researchers was with Canada. In the mapping thematic network, among the subjects under the study of the university, environmental health, public and professional, the highest degree of centrality, and pharmacy and pharmacology had the highest betweeness centrality. Conclusion: Considering the high level of collaboration and co-authorship between researchers of Kurdistan University of Medical Sciences, it is recommended to create the essential ground for initiating and consolidating international and national collaboration and setting up scientific and expert colleague groups. Keywords: Scientific Collaboration, Co-authorship Network, Kurdistan University of Medical Sciences
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.032 | 0.042 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".