Computer Science > Digital Libraries
[Submitted on 19 Jun 2019 (v1), last revised 20 Jan 2020 (this version, v2)]
Title:Paper-Patent Citation Linkages as Early Signs for Predicting Delayed Recognized Knowledge: Macro and Micro Evidence
View PDFAbstract:In this study, we investigate the extent to which patent citations to papers can serve as early signs for predicting delayed recognized knowledge in science using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay. We provide a macro (Science/Nature papers dataset) and micro (a case chosen from the dataset) evidence on paper-patent citation linkages as early signs for predicting delayed recognized knowledge in science. It appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and earlier by a patent rather than by a scientific paper (also called "prince"). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called "flash in the pan", i.e., instant recognition. It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focused fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of "science sleeping"-"technology [...]. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.
Submission history
From: Robin Haunschild [view email][v1] Wed, 19 Jun 2019 07:45:43 UTC (815 KB)
[v2] Mon, 20 Jan 2020 13:19:43 UTC (1,383 KB)
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