What semantic analysis can tell us about long term trends in the global STI policy agenda

Leonid Gokhberg, Dirk Meissner () and Ilya Kuzminov
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Dirk Meissner: National Research University Higher School of Economics
Ilya Kuzminov: National Research University Higher School of Economics

The Journal of Technology Transfer, 2023, vol. 48, issue 6, No 13, 2249-2277

Abstract: Abstract The scope, complexity and the “volume” of knowledge accumulated render producing an overview of the core themes of science, technology and innovation policies difficult. Reviews of this policy domain mostly either refer to general issues without deep immersion into details or focus on specific narrower aspects. The paper uses semantic analysis to identify major themes of science, technology and innovation policies over the last three decades and to trace their evolution towards current policy setting. We use semantic tools for processing and analysing documents produced by one of the major and highly reputable international expert bodies, the OECD Working Party on Technology and Innovation Policy. We show that selected themes remain in the mainstream even though being affected by regular policy adjustments and refinements and which disappear or appear with new challenges and expected solutions. Other themes appear niche or exotic themes which are under discussion for some time only.

Keywords: Science policy; Technology and innovation policies; Evidence based policy; OECD Working Party on Technology and Innovation Policy; Big data analysis; Semantic analysis; Text mining; International policy agenda (search for similar items in EconPapers)
JEL-codes: F00 L5 O1 O3 O4 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10961-022-09959-5

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