NIH拨款批评的网络分析

D. Malikireddy, M. Jens, Amarette Filut, Anupama Bhattacharya, Elizabeth L. Pier, You Geon Lee, M. Carnes, A. Kaatz
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引用次数: 2

摘要

网络分析在研究许多社会现象方面有着广泛的应用。我们的研究重点是调查为什么高素质的女性和种族/少数民族往往在同行评审过程中表现更差,比如在科学资助方面,这限制了她们参与研究事业。我们之前的工作表明,性别和种族偏见可以在评论者的叙事批评中检测到,但我们的工作尚未利用各种学习算法进行文本分析的能力。为此,我们展示了网络算法在研究审稿人对提交给美国国立卫生研究院(NIH)的拨款申请的书面评论方面的有用性的初步证据。我们构建了词共现网络,并表明网络测量因求职者性别而异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network analysis of NIH grant critiques
Network analysis has widespread applications for studying many social phenomena. Our research is focused on investigating why highly qualified women and racial/ethnic minorities tend to fare worse in peer review processes, such as for scientific grants, which limits their participation in research careers. Our prior work shows that gender and racial bias can be detected in reviewers' narrative critiques, but our work has yet to harness the power of varied learning algorithms for text analysis. To this end, we show preliminary evidence of the usefulness of network algorithms to study reviewers' written critiques of grant applications submitted to the U.S. National Institutes of Health (NIH). We construct word co-occurrence networks and show that network measures vary by applicant sex.
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