HackerPapers

arXiv:1909.02063cs.SI

The Diversity-Innovation Paradox in Science

Bas Hofstra, Vivek V. Kulkarni, Sebastian Munoz-Najar Galvez, Bryan He, Dan Jurafsky, Daniel A. McFarland

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Prior work finds a diversity paradox: diversity breeds innovation, and yet, underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-population of ~1.2 million US doctoral recipients from 1977-2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: e.g., novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity's role in innovation and partly explains the underrepresentation of some groups in academia.

arXiv comments
Updated paper; tightened up terminology, added better theoretical explanation, tested for a mechanism in the updated paper, added robustness analyses, updated and improved metrics across the board
Journal reference
DOI
doi:10.1073/pnas.1915378117
Subjects
cs.SISocial and Information Networks cs.CLComputation and Language stat.APApplications (Statistics) stat.MLMachine Learning (Statistics)

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Apr 2020 to Apr 2020.

Comments linking this paper, per year.

Discussed in comments1 thread

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.