World Science against COVID-19: Gender and Geographical Distribution of Research
Bibliographic record
Abstract
Abstract In just a year and a half, an enormous volume of scientific research has been generated throughout the world to study a virus/disease that turned into a pandemic. All the articles on COVID-19 or SARS-CoV-2 included in the SCI-EXPANDED database (Web of Science), signed by more than a third of a million of authorships, were analyzed. Gender could be identified in 92% of the authorships. Women represent 40% of all authors, a similar proportion as first authors, but just 30% as last/senior authors. The pattern of collaboration shows an interesting finding: when a woman signs as a first or last/senior author, the article byline approximates gender parity According to the corresponding address, the USA shares 22.8% of all world articles, followed by China (14.4%), Italy (7.8%), the UK (5.8%), India (4.2%), Spain (3.8%), Germany (3.6%), France (2.9%), Turkey (2.5%), and Canada (2.4%). Despite their short lives, the papers received an average of 11 citations. The high impact of papers from China is striking (25.1 citations; the UK, 12.4 citations; the USA, 11.3 citations), presumably because the disease emerged in China, and the first publications (very cited) came from there.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".