:Data show that science, technology, engineering and math (STEM) postsecondary training programs lack gender and racial/ethnic diversity. Recent policy efforts are aimed at creating more inclusive environments for underrepresented groups in STEM and several national reports highlight progress. We argue that prior analyses have not considered institutional contexts and changes in the demographics of students enrolled in higher education more broadly. We propose new measures of gender and racial/ethnic parity in the computing fields. Using these measures, we find that while computing fields have made progress in the number of female students and students of color receiving degrees, gender and racial/ethnic parity has changed little and, in some cases, declined. We conclude with recommendations for researchers, practitioners, and policymakers.
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Ten simple rules for building an antiracist lab
Demographics of the science, technology, engineering, and mathematics (STEM) workforce and student body in the US and Europe continue to show severe underrepresentation of Black, Indigenous, and people of color (BIPOC). Among the documented causes of the persistent lack of diversity in STEM are bias, discrimination, and harassment of members of underrepresented minority groups (URMs). These issues persist due to continued marginalization, power imbalances, and lack of adequate policies against misconduct in academic and other scientific institutions. All scientists can play important roles in reversing this trend by shifting the culture of academic workplaces to intentionally implement equitable and inclusive policies, set norms for acceptable workplace conduct, and provide opportunities for mentorship and networking. As scientists are increasingly acknowledging the lack of racial and ethnic diversity in science, there is a need for clear direction on how to take antiracist action. Here we present 10 rules to help labs develop antiracists policies and action in an effort to promote racial and ethnic diversity, equity, and inclusion in science.
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- Award ID(s):
- 1725650
- PAR ID:
- 10286085
- Date Published:
- Journal Name:
- PLoS computational biology
- Volume:
- 16
- Issue:
- 10
- ISSN:
- 1553-7358
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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