A comprehensive analysis of manuscript submissions to the American Journal of Public Health has uncovered significant disparities in both the quantity and acceptance rates of submissions based on the predicted race, ethnicity, and gender of authors.
Dominance of White Authors in Submissions
The study scrutinized 17,667 manuscripts submitted between 2013 and 2022 using advanced machine-learning algorithms to predict authors’ race/ethnicity and gender from their names. Findings indicate that White authors not only submitted the majority of manuscripts but also enjoyed the highest acceptance rate at 21.1%. Conversely, submissions from Asian authors were the least accepted, with a rate of 14.9%.
Persistent Gender Disparities Identified
Gender analysis revealed that women, despite being the majority of authors, faced lower acceptance rates (17.9%) compared to men (20.5%). This trend held steady across most racial and ethnic groups, highlighting ongoing gender inequalities in academic publishing.
• Machine-learning algorithms consistently showed similar disparity patterns across different methodologies.
• Algorithmic biases and inaccuracies limited the precise prediction of race and ethnicity.
• Predicted White and male authors maintained the highest manuscript acceptance rates.
The research emphasizes that while machine-learning tools are effective in identifying authorship disparities, their current limitations necessitate the integration of self-reported demographic data to enhance accuracy. This combined approach could provide a more reliable foundation for analyzing and addressing inequities in scientific publishing.
Promoting diversity among authors is crucial for enriching scientific discourse and fostering an inclusive academic environment. Publishers and researchers must work collaboratively to address these disparities by potentially revising submission and review processes to mitigate inherent biases. Future efforts should focus on refining predictive algorithms and investigating the root causes of these persistent inequities to ensure a more balanced and equitable publication landscape.
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