Intellectual Structure of Coronavirus Research: A Perspective From an Author Cocitation Analysis
Bibliographic record
Abstract
The present study examines the intellectual structure of research on coronavirus, as revealed from an author co-citation analysis using citation data retrieved from the Web of Science Core Collection and mapped to the PubMed database. Four major dimensions are identified: I) outbreaks, II) viral structure and function, III) vaccine and therapeutic development, and IV) coronaviruses found in a range of animals. The "outbreaks" dimension is by far the most prominent, dominated by reports on the three recent major outbreaks: COVID-19, severe acute respiratory syndrome, and Middle East respiratory syndrome. The focus of research on major outbreaks is on public health and clinical research, with focus on disease characterization, diagnosis, transmission, and clinical course. Notably, certain clinically important areas, such as mental health during outbreaks and viral surveillance, among others, did not stand out as identifiable specialties or topics in the coronavirus research landscape. Results from this study should contribute to the understanding of the coronavirus research landscape and to the identification of strengths and weaknesses of current research on COVID-19.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.036 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.145 | 0.500 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".