Scientometric Study of World Research Publications on COVID-19 from the Scopus Database for the Period of 2019-2021
Bibliographic record
Abstract
The present paper analyzes the scientometric study of COVID-19 research publications from the Scopus database between 2019 and 2021 with 248966 research publications and 2428009 citations. During the study period maximum of 163085(65.50%) research publications are contributed in the year 2021, followed by 85819(34.47%) publications in the year 2020, and 62(0.02) publications in the year 2019. The average research publication per year is 82989. The maximum of 60964(27.26%) contributions are from the United States, the citations are 84795(3.73), CPP is 1.39, H- index is 87 and RCI is 0.14. The maximum of 22753(18.69%) contributions are Biochemistry, Genetics, and Molecular Biology. The maximum of 266(13.23%) contributions are Mahase, E, from the United States. A maximum of 157579(63.29%) research publications are contributed by articles. The maximum of 3405(11.48%) contributions are the Harvard Medical School, the maximum of 3178(15.74%) contributions from are International Journal of Environmental Research and Public Health, the highest citations were 36889(10.98%) in the Journal of Medical Virology, with the CPP being 30.51, h-index is 82 and RCI is 1.83.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.115 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".