Using visualization technique to communicate the conceptual structure of SARS-CoV-2 to multidisciplinary audience and lessons from the pandemic for future preparedness
Bibliographic record
Abstract
Several publications on the concept and structure of SARS-CoV-2 and COVID-19 over the past three years target medical and biomedical scientists, and rightly so, as experts in search of solutions made efforts to understand the molecular structure of the coronavirus. The multidisciplinary audience who needs help understanding the scientific discourse and the complexity of SARS-CoV-2 is left to guess in the dark. Studies show that a lack of proper understanding of the pandemic can have several consequences, including accepting conspiracy theories, misinformation, negative attitudes against public health safety measures, and the COVID-19 vaccine hesitation. This study uses metadata extracted from published documents on the concepts and structure of COVID-19 indexed on the Web of Science between 2020 to 2021 to create an abstract visual metaphor about the pandemic. Based on the cognitive connection theory, we develop a model and visualization that explains the conceptual structure of SARS-CoV-2 and COVID-19 for the non-biomedical multidisciplinary audience. The visual analytics highlights the concepts, characteristics, and interrelationships on a network map, connecting some past viral/coronavirus pandemics and epidemics, particularly H1N1, SARS-CoV, and MERS-CoV. The conceptual model and visualization generate insight and understanding of the ongoing pandemic for multidisciplinary audiences.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".