Assessing the interdependencies between scientific disciplinary profiles

Cinzia Daraio, Francesco Fabbri, Giulia Gavazzi, Maria Grazia Izzo, Luca Leuzzi, Giammarco Quaglia and Giancarlo Ruocco ()
Additional contact information
Cinzia Daraio: Sapienza University of Rome
Francesco Fabbri: Sapienza University of Rome
Giulia Gavazzi: Sapienza University of Rome
Maria Grazia Izzo: Sapienza University of Rome
Luca Leuzzi: CNR-NANOTEC
Giammarco Quaglia: Sapienza University of Rome
Giancarlo Ruocco: Fondazione Istituto Italiano di Tecnologia (IIT)

Scientometrics, 2018, vol. 116, issue 3, No 19, 1785-1803

Abstract: Abstract The investigation of the dynamics of national disciplinary profiles is at the forefront in quantitative investigations of science. We propose a new approach to investigate the complex interactions among scientific disciplinary profiles. The approach is based on recent pseudo-likelihood techniques introduced in the framework of machine learning and complex systems. We infer, in a Bayesian framework, the network topology and the related interdependencies among national disciplinary profiles. We analyse data extracted from the Incites database which relate to the national scientific production of most productive world countries at disciplinary level over the period 1992–2016.

Keywords: Disciplinary profiles; Country-level studies; Pseudo-likelihood estimation; Incites (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-018-2816-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:scient:v:116:y:2018:i:3:d:10.1007_s11192-018-2816-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-018-2816-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().