Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references

Julie Callaert (), Joris Grouwels and Bart Looy
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Julie Callaert: Faculty of Business and Economics, K.U. Leuven
Joris Grouwels: Faculty of Business and Economics, K.U. Leuven
Bart Looy: Faculty of Business and Economics, K.U. Leuven

Scientometrics, 2012, vol. 91, issue 2, No 7, 383-398

Abstract: Abstract Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science–technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered ‘scientific’. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science–technology interactions.

Keywords: Science–technology interaction; Non-patent references; Indicators; Machine learning; 68U15 (search for similar items in EconPapers)
JEL-codes: O32 O34 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (18)

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DOI: 10.1007/s11192-011-0573-9

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