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.

中文翻译：

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科学中的多样性创新悖论

先前的工作发现了一个多样性悖论：多样性孕育了创新，然而，使组织多样化的代表性不足的群体在他们的职业生涯中不太成功。多样性悖论是否也适用于科学家？我们通过利用 1977 年至 2015 年间近 120 万名美国博士获得者并跟随他们的职业生涯进入出版和教职职位来研究这一点。我们使用文本分析和机器学习来回答一系列问题：我们如何检测科学创新？代表性不足的群体是否更有可能产生科学创新？代表性不足的群体的创新是否被采纳和奖励？我们的分析表明，代表性不足的群体产生更高的科学新颖性。然而，他们的新贡献被贬值和打折：例如，其他学者对性别和种族少数群体的新贡献的接受率低于性别和种族多数群体的新贡献，并且与多数群体相比，性别和种族少数群体具有同等影响力的贡献不太可能导致成功的科学事业。这些结果表明，学术职业中可能存在无根据的分层再现，这低估了多样性在创新中的作用，并部分解释了学术界某些群体代表性不足的原因。