LMIB, NLSDE, BDBC, and Institute of Artificial Intelligence, Beihang University, 100191, Beijing, China.
Peng Cheng Laboratory, 518055, Shenzhen, China.
Nat Commun. 2022 Aug 20;13(1):4907. doi: 10.1038/s41467-022-32604-6.
While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries.
尽管科学界的不平等现象很常见，但大多数旨在理解这些现象的努力都将科学家视为孤立的个体，忽略了合作的网络效应。在这里，我们开发了模型，可以将个人科学家的生产力（定义为论文数量）和知名度（指高影响力出版物）的网络效应与其合作网络分离开来。我们发现，处于职业生涯中期的研究人员在生产力和知名度上的性别差异，可以很大程度上用他们的合著网络差异来解释。因此，合作网络是一种社会资本形式，我们发现有证据表明这种社会资本可以从资深合作者转移到初级合作者，而且随着研究人员年龄的增长，这种好处会逐渐减少。合作网络效应也可以解释知名机构的研究人员在生产力和知名度方面的优势的很大一部分。这些结果突出了社会网络在推动科学界不平等方面的重要作用，并表明合作网络代表了一种重要的不平等分配的社会资本形式，它塑造了谁做出了什么样的科学发现。
