Are Pay-Walled Doors of Access Open During the Pandemic? Analysing the Open-Access Landscape of COVID-19 Research
Bibliographic record
Abstract
Open access (OA) to research results is indispensable for knowing more about COVID-19 and ways to contain it. The study investigates the OA status of the research output on COVID-19 using the Web of Science. The results show that about 85 per cent of the publications are available as OA, which shows a decline over time. Almost an equal proportion of articles are funded and non-funded, with the Department of Health and Human Services and the National Institutes of Health, both in the United States, as the leading sponsors. Although the United States and China were the top contributors, Sweden and the Netherlands share the highest percentage of OA articles. Among publishers, Elsevier, Springer Nature, the Multidisciplinary Publishing Institute, and Wiley were the leading OA publishers, and universities mainly dominated OA research on COVID-19. This study will be helpful for researchers and policymakers to identify the leading contributors to OA research during public health emergencies of international concern.
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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 | 0.038 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".