The Gender of COVID-19 Experts in Newspaper Articles: A Descriptive Cross-Sectional Study
Bibliographic record
Abstract
Abstract Background: Pre-existing gender-based disparities in academia may have worsened during the COVID-19 pandemic. There is anecdotal and peer-reviewed evidence that women in academia have been underrepresented in prestigious, pandemic-related opportunities. Being citated as an expert source in newspaper articles about COVID-19 may increase an individual's research or leadership profile. In addition, visibility in a newspaper article is an important component of representation in academia. Objective: We sought to determine whether women were underrepresented as COVID-19 expert sources in print newspapers in the United States. Design: We undertook a cross-sectional study of English-language newspaper articles that addressed the COVID-19 pandemic that were published in the top ten most widely read newspapers in the United States between Apr 1 and Apr 15, 2020. Main Measures: We extracted the names of all people cited as expert sources and categorized each expert sources as men, women, or another gender based on pronoun usage within the article or on a business, university, or organization website. Key Results: Of 2,297 expert sources identified, 35.9% (95% CI 33.9-37.8%; n=824) were women, 63.7% were men (95% CI 61.8-65.7%; n=1,464) and for 0.4%, gender could not be assigned (n=9). After removing duplicate experts, 1,738 unique individuals were cited, of which 34.6% were women (95% CI 32.3-36.8%; n=601), 64.9% were men (95% CI 62.7-67.1%; n=1,128), and 0.05% whose gender was unknown (n=9). Of articles with multiple experts referenced (n=374), 102 cited only men experts (27.3%) and 44 cited only women experts (11.8%).Conclusions: Altogether, this result supports that men are overrepresented compared to women as COVID-19 experts in newspaper articles.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".