Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia

Moilanen Mikko, Østbye Stein and Simonen Jaakko

Regional Studies, 2022, vol. 56, issue 9, 1429-1441

Abstract: The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00343404.2021.1925237 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:regstd:v:56:y:2022:i:9:p:1429-1441

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CRES20

DOI: 10.1080/00343404.2021.1925237

Access Statistics for this article

Regional Studies is currently edited by Ivan Turok

More articles in Regional Studies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().