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Understanding Geographical Patterns of Scientific Collaboration in the field of Artificial Intelligence


Understanding Geographical Patterns of Scientific Collaboration in the field of Artificial Intelligence

Toobaee, Mohammadmahdi (2022) Understanding Geographical Patterns of Scientific Collaboration in the field of Artificial Intelligence. Masters thesis, Concordia University.

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The role of geographical proximity in facilitating inter-regional or inter-organizational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level has not been addressed so far. Using co-publication data of AI researchers from 2000 to 2019, first, the effect of geographical proximity on the chance of future scientific collaboration among researchers was studied. The logit regression and machine learning classification results show that geographical distance is an essential impediment to scientific collaboration at the individual level despite the tremendous improvements in transportation and communication technologies during recent decades. Second, the interplay between geographical proximity and network proximity was examined to see whether network proximity can substitute geographical proximity in encouraging long-distance scientific collaborations. The results show that the effect of network proximity on the likelihood of scientific collaboration increases with geographical distance, implying that network proximity acts as a substitute for geographical proximity. Therefore, policies aiming at encouraging long-distance collaborations could positively affect scientific collaboration and future knowledge production.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Toobaee, Mohammadmahdi
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:10 February 2022
Thesis Supervisor(s):Schiffauerova, Andrea and Ebadi, Ashkan
ID Code:990307
Deposited By: Mohammadmahdi Toobaee
Deposited On:16 Jun 2022 15:18
Last Modified:16 Jun 2022 15:18


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