Scholarly Research Output on COVID-2019: The Published Literature Analysis on the ISI Web of Science Databases
Bibliographic record
Abstract
The research portrays evaluation of published literature on the topic of COVID-2019 globally. The ISI Web of sciences database was used to access the published literature till December 01, 2020. The types of publications included in the research were, editorials, letters, reviews, articles, case reports, abstracts, and books. The indicators based on the factors; publication period, the most contributing authors, most publishing institutes, countries’ contributions, and research journals titles. A total of 82371 documents were retrieved from the database. The USA has produced 16229 documents that are the almost 20% of the total publications. The contribution on research from China is at second position with the numbers of 6994 (8.491%). Italy in research productivity remained third with the number of 5925 (7.193 %). England, India, Canada, Spain Germany, Australia, and France remained in the top ten productive countries in the publication of Covid-2019 respectively. The research publications percentage of these seven countries remained 2.721- 7.005 percent.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.173 | 0.234 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.026 |
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".