Knowledge integration and diffusion structures of interdisciplinary research: A large‐scale analysis based on propensity score matching
使用倾向得分匹配方法分析2005年微软学术图谱中的期刊论文，发现跨学科研究的知识整合与扩散模式因学科而异，对制定跨学科研究政策有启示。
Abstract While facilitating science, interdisciplinary research (IDR) has a heavier cognitive burden for researchers compared to unidisciplinary research (UDR). Yet, little has been known about patterns of knowledge integration and diffusion structures of IDR. Here we adopt a causal inference strategy, namely propensity score matching, with all journal publications in 2005 in Microsoft Academic Graph to better understand the IDR effect in various research fields. We use the diversity of reference fields of one paper as the proxy of the paper's interdisciplinarity and estimate the effect of a research article being IDR on its knowledge integration and diffusion measured by its high‐order citation/reference cascade. We find that, in disciplines where IDR articles are less popular, such as mathematics, physics, and chemistry, IDR needs a more extensive knowledge base than UDR to gain a similar number of citations. In disciplines where IDR articles are more popular, for example, psychology, geology, biology, and economics, a small knowledge base is enough for a high‐impact IDR article. As to knowledge diffusion, no matter whether IDR or UDR, a more extensive knowledge base leads to stronger knowledge diffusion ability. Findings imply potential drawbacks of pure interdisciplinarity‐oriented research policy; rather, the establishment of policies may vary across disciplines.