Research Output, Key Topics, and Trends in Productivity, Visibility, and Collaboration in Social Sciences Research on COVID-19: A Scientometric Analysis and Visualization
Bibliographic record
Abstract
COVID-19 has caused a surge in scientific publications, with increased collaboration. This study aims to elucidate scientific output, focal topics, emerging themes, and trends and patterns of productivity, visibility, and collaboration within social sciences research (SSR) on COVID-19. A scientometric analysis was conducted utilizing Biblioshiny and VOSviewer software. About 65,742 records published on WOS between 2020 and 2022 were analyzed. Topics such as “telehealth,”“well-being,” and “inequalities,” were among the key topics while “interventions” and “mental well-being” were among emerging key topics. Collaboration patterns were regional. Harvard Medical School, the University of Toronto, and the University of Oxford emerge as leaders in collaboration, productivity, and influence. The USA, Italy, India, Spain, and Brazil serve as regional hubs for facilitating collaboration. The USA, England, and China exhibit leadership and influence, playing pivotal roles In shaping the global research. These findings are important for policymakers, funding agencies, and researchers in cultivating future research topics and collaborative efforts. The findings can inform strategic decision-making, resource allocation, and policy development to address present and future health crises. Additionally, these efforts contribute to advancing global sustainability initiatives and promoting human well-being. Encouraging international collaboration is essential, particularly for tackling the regional challenges encountered by countries with limited domestic research capabilities. Future research can improve the methodology used by di-versifying data sources beyond WOS.
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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 | 0.047 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.175 | 0.271 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".