Insights from the co-authorship network of the Italian academic statisticians

Silvia Bacci (), Bruno Bertaccini () and Alessandra Petrucci ()
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Silvia Bacci: University of Florence
Bruno Bertaccini: University of Florence
Alessandra Petrucci: University of Florence

Scientometrics, 2023, vol. 128, issue 8, No 5, 4269-4303

Abstract: Abstract Nowadays, new technologies have favored communication among scholars from different universities and countries, and huge amount of data and scientific works have become more and more accessible. This has led to an increase in the multidisciplinarity of research products, but often also to a more specialized level of knowledge of the scholars. Therefore, while belonging to the same disciplinary field, scholars may present different working styles and willingness to collaborate according to their specific topics of interest. This plays a particularly relevant role in Italy, where tenured scholars in academic institutions are classified in sub-fields that, in turn, may be aggregated for purposes of recruitment and career advancement. Aim of this contribution is to propose a methodological approach to understand if the work and collaborative style of academic scholars belonging to different sub-fields is really so similar as to justify their grouping. For illustrative purposes, we focus on the co-authorship network of Italian academic statisticians relying on the database of scientific works published since 1990 until 2021 and downloaded by SCOPUS. From this database, we obtain a network composed of 758 nodes and 1730 edges. Some network measures at node level representing the work and collaborative style of scholars (i.e., number of publications, degree, degree strength, some centrality indices, transitivity, and external-internal index) are explained through quantile regression models. Results provide policy makers with useful insights on which sub-fields present significant differences in terms of research interests and collaborative style, thus not justifying their aggregation for recruitment and career advancement purposes.

Keywords: Bibliographic data source; Network analysis; Quantile regression model; Scientific collaboration; 62G08; 91D30 (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-023-04761-y

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