Gender Balance and Readability of COVID-19 Scientific Publishing: A Quantitative Analysis of 90,000 Preprint Manuscripts
Bibliographic record
Abstract
Abstract Releasing preprints is a popular way to hasten the speed of research but may carry hidden risks for public discourse. The COVID-19 pandemic caused by the novel SARS-CoV-2 infection highlighted the risk of rushing the publication of unvalidated findings, leading to damaging scientific miscommunication in the most extreme scenarios. Several high-profile preprints, later found to be deeply flawed, have indeed exacerbated widespread skepticism about the risks of the COVID-19 disease – at great cost to public health. Here, preprint article quality during the pandemic is examined by distinguishing papers related to COVID-19 from other research studies. Importantly, our analysis also investigated possible factors contributing to manuscript quality by assessing the relationship between preprint quality and gender balance in authorship within each research discipline. Using a comprehensive data set of preprint articles from medRxiv and bioRxiv from January to May 2020, we construct both a new index of manuscript quality including length, readability, and spelling correctness and a measure of gender mix among a manuscript’s authors. We find that papers related to COVID-19 are less well-written than unrelated papers, but that this gap is significantly mitigated by teams with better gender balance, even when controlling for variation by research discipline. Beyond contributing to a systematic evaluation of scientific publishing and dissemination, our results have broader implications for gender and representation as the pandemic has led female researchers to bear more responsibility for childcare under lockdown, inducing additional stress and causing disproportionate harm to women in science.
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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 | 0.083 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".