COVID-19 pandemic and the answer of science: a year in review
Bibliographic record
Abstract
The world is looking forward to a prompt response by the scientific community in order to overcome the first pandemic of the 21st century. This study aimed to provide an overview of scientific output on COVID-19 during its first year. We assembled information regarding 60,830 articles related to COVID-19 indexed in the WoS database from January 24 to December 13, 2020. Only 4 countries accounted for about 60% of the articles (USA, China, Italy, and England) and 12 countries accounted for about 95% of the world scientific output on COVID-19 (USA, China, Italy, England, India, Canada, Germany, Spain, Australia, Brazil, Iran, and Turkey). 25 research centers around the world contributed with more than 500 papers on COVID-19. Papers were scattered throughout 6,133 journals, with 12 journals with > 250 articles. 20 articles (0.03%) have already received more than the 1,000 citations. The response of the scientific endeavor to this acute global public health emergency has been fast and robust. The overview provided by the analysis of the scientific response to the pandemic may contribute to further studies aiming to evaluate the impact and changes in the scientific endeavor for the next years in light of the forthcoming new world framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.400 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".