Assessing COVID-19 Research Outputs of Iran’s Top Universities in Social Media: An Altmetric Study
Bibliographic record
Abstract
Background and aim: Scientific infrastructures of societies would be mainly constructed in universities and academic environments. As a result, considering research outputs and performances in academic departments are of high significance. The current study aimed at investigating the research outputs of Iran’s top universities as mentioned in social media on the COVID-19 pandemic. Materials and methods: Scientometric (Quantity and Scientific productions) and Altmetric (Mentions Bookmarks and altmetric scores) indices were considered as the research methodology. The population of this study was COVID-19 research outputs of the Scopus database from the top universities of Iran including Tehran University, Shahid Beheshti University, Tarbiat Modares University, Allameh Tabataba’i University, Isfahan University, Shiraz University, Ferdowsi University of Mashhad and Tabriz University in 2020. Data were collected from the Scopus and Altmetric Explorer databases and were analyzed using the descriptive statistics and Spearman correlation test. Findings: The University of Tehran had the highest rank with 1409 research outputs on the COVID-19. The highest international cooperation was made with the United States, Britain and Canada, respectively. The major social media used were Twitter and Mendeley. The results of the Spearman correlation test revealed that there was a statistically significant relationship between citation index and altmetric score (p-value< 0/05) regarding the research outputs of Tehran University, Shahid Beheshti University, Shiraz University and Ferdowsi University of Mashhad. Conclusion: Publications receiving more citations are not necessarily mentioned in social media.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".