Evaluating the Scientific Collaborations among Type-1 Medical Sciences Universities at National and International Levels Based on Indexed Documents in ISI Web of Knowledge During 2004-2008
Bibliographic record
Abstract
Purpose: To investigate the scientific collaborations (for the papers indexed in ISI web of knowledge) among researchers from type-1 universities of medical sciences during 2004-2008, the present study was conducted. Methodology: Webometrics based upon the co-authorship index was used. The population under study included the Tehran, Shahid Beheshti, Iran, Shiraz, Isfahan, Mashhad, Tabriz, JondiShapour (Ahvaz) and Kerman Universities of Medical Sciences. To collect data, all documents related to each of these universities were initially retrieved from the ISI web of knowledge database. Then the statistical methods of abundance distribution and abundance percentage in form of table and graph, and Spearman’s correlation coefficient for analysis of the relations among the variables were used. Findings: Considering the results, highest ratio of national scientific collaborations to total scientific publications with descending order was assigned to the Kerman, Iran, and Shahid Beheshti Universities of Medical Sciences. Most international scientific collaborations were made with researchers from the US, UK, and Canada. Pharmacology and pharmacy were the fields with highest rate of scientific collaboration, both at national and international levels. Results of Pearson’s correlation test indicate a significant relationship between scientific publications of the universities and their national scientific collaborations, between scientific publications of the universities and their international scientific collaborations, and between their national and international scientific collaborations. Originality/Value: In addition to providing an overview of the status of scientific collaborations among researchers in type-1 universities of medical sciences, the present study has identified the fields which have received attention from researchers in their scientific collaborations.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 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 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".