Dimensions of the Scientific Collaborations of the Researchers Affiliated with Shiraz University of Medical Sciences
Bibliographic record
Abstract
Researchers at medical universities are highly active in scientific collaborations at the national, regional, and international levels. Iranian Medical researchers pay diligent attention to scientific collaborations at all levels. The present study aimed to investigate various dimensions of scientific collaborations of the researchers at Shiraz University of Medical Sciences (SUMS). The dimensions include the patterns and levels of national and international collaborations, interdisciplinary interactions, the relationship between geographical distance and scientific collaboration, and the interdisciplinarity pattern of international collaborations. The study adopted a descriptive-analytical method. The data were collected using scientometric measures. The research population consisted of 4499 journal articles in Web of Science (WoS) authored by SUMS researchers during 2014-2018. The VOSviewer was applied to analyze the data and visualize the networks. The results revealed that national collaboration was the dominant pattern. The results showed a desirable ratio of scientific collaborations to all publications (52%). The authors mostly tended to collaborate with American researchers. The majority of interdisciplinary collaborations were observed in the microbiology field. The results suggested that geographical distance did not affect scientific collaborations at the national and international levels (P>0.05). At the international level, SUMS researchers had the highest collaboration with the University of Manitoba and Tehran University at the national level. The results suggested that research policymakers at SUMS should prioritize research policies toward scientific collaborations at all levels and fields to share and synergize knowledge. https://dorl.net/dor/20.1001.1.20088302.2021.19.2.3.1
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".