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Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology

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Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology

Crespo Neira, Elva Luz (2016) Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology. Masters thesis, Concordia University.

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Abstract

We address the question of whether or not geographical, cognitive, and collaborative proximity have an impact on citation probability in the scientific writings of Canadian nanotechnology. Even though a number of studies in the proximity literature deal with the effects of spatial distance, scientific specialization, and social network structure, to our knowledge no one has combined all three to explore the production of academic information.

We generate a feature framework based on measurements from these factors, relying on statistical and classification approaches to assess their influence on effective citations. Specifically, by means of applying binary regression models along with tree-based machine learning algorithms, we found statistical significance proving that these features have both a verifiable impact and predictive potential. Importantly, our work is the first one that we have seen combining these techniques to infer the establishment of positive citation links. Moreover, we employed inductive network analysis comprehensively to examine the co-authorship links between authors publishing in nanoscience, considering additional network metrics to the ones usually adopted in the literature.

Our findings reveal that cognitive proximity, closely followed by the collaborative aspect, are the most important elements inducing Canadian scholars to cite, with geography sometimes acting as their base. Our results enable us to reach better understanding related to the citation behavior of the nanoresearch community in Canada, making our work a valuable contribution to scholarly literature, also giving us ground to make policy recommendations.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Crespo Neira, Elva Luz
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:25 April 2016
Thesis Supervisor(s):Schiffauerova, Andrea and Beaudry, Catherine
Keywords:proximity, citation, social network analysis, nanotechnology, machine learning, bibliometrics, geography, collaboration, cognitive
ID Code:981187
Deposited By: ELVA CRESPO
Deposited On:15 Jun 2016 19:47
Last Modified:18 Jan 2018 17:52

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