The Pandemic of the COVID-19 Literature: A Bibliometric Analysis Running Title: Bibliometric Analysis of the COVID-19 Literature
Bibliographic record
Abstract
Abstract BackgroundThe research interest in COVID-19, one of the most serious pandemics in recent human history, is unprecedented. This study aims to determine the volume of COVID-19 research and to assess the characteristics of its production and publication.MethodsWe searched Scopus, Embase, PubMed, and the Web of Science databases for publications, up to August 20, 2020. We included all types of documents except corrections, interviews, personal narratives, and retracted publications. We analyzed publication count, type, status, research themes, publication venues, authorship trends, language, institutions, countries, collaboration, and funding.ResultsOf 40,519 eligible documents, 49% were original articles. Forty-nine percent of the original articles and reviews were published in top quartile journals, and 19% were single-authored. More than half of the documents were produced in the United States, China, the United Kingdom, and Italy. Twenty-two percent of the documents involved international collaboration and 17% reported financial support by at least one agency, with the National Natural Science Foundation of China being the most frequently reported funding source (n=982). There are already more documents published on COVID19 than documents ever published on the Ebola, MERS, HIN1, and SARS combined.ConclusionsThe first few months’ research output on COVID-19 is relatively large and originated mostly from four countries. Single-authored publications, international collaboration, and governmental funding activities were relatively common.
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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 | 0.020 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.232 | 0.265 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".