SMU Data Science Review


Women and minorities within STEM disciplines historically encounter obstacles in academic advancement, a situation compounded by the COVID-19 pandemic due to the imposition of additional responsibilities like caregiving. This study meticulously probes into the pandemic's influence on traditional academic productivity metrics – specifically publication and submission frequency, citation volume, and leadership in scholarly entities, by employing Natural Language Processing to extract and analyze data from key journals within various scientific domains. A critical revelation from the research indicates a notable downturn in publication activity during 2021, potentially attributed to pandemic-induced disruptions, with a compensatory surge observed in 2022. Although a gradual ascendancy towards gender parity in academic authorship was observed, the journey toward substantive equality is confronted with future challenges, including policy shifts and societal factors. This investigation not only illuminates the nuanced disparities in academic publishing but also endeavors to guide institutional strategies towards genuinely equitable promotion, tenure policies, and practices, ensuring that the academic merit of all scholars, regardless of gender or minority status, is acknowledged and rewarded.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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