Drawing the scientific collaboration map of researchers at the University of Tehran: A short communication
Bibliographic record
Abstract
Background and aim: The aim of this study was to illustrate the status of scientific collaboration of the University of Tehran in International Scientific Associations using Web of Science (WoS). Materials and methods: In this applied study, scientometric techniques were used to draw a scientific map. The statistical population included all scientific outputs of researchers affiliated with the University of Tehran from 2015 to 2019. The data were analyzed using Excel and VOSviewer. Findings: The analysis of the findings showed that about 32% of the total outputs of the University of Tehran were done through international collaboration. Among the domestic institutions, the Islamic Azad University and among the foreign institutions, the "National Center for French Scientific Research" had the most collaboration with the researchers of the University of Tehran. Iranian researchers had the most collaboration with researchers from the United States, Canada and Germany. The fields of "Engineering", "Material Science" and "Chemistry" had the most collaboration with foreign researchers. Conclusion: The scientific collaboration of the University of Tehran with foreign countries is in a favorable condition.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".