Knowledge integration and diffusion structures of interdisciplinary research: A large‐scale analysis based on propensity score matching

Abstract

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.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.