ECOOM-day, 20 November 2015, Antwerp. Clustering by publication patterns of senior authors in the Social Sciences and Humanities Frederik Verleysen* &

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Presentation transcript:

ECOOM-day, 20 November 2015, Antwerp

Clustering by publication patterns of senior authors in the Social Sciences and Humanities Frederik Verleysen* & Arie Weeren** *ECOOM - University of Antwerp **StatUA - University of Antwerp

Introduction Goal: to map diversity in scholarly publication patterns beyond SS vs. H beyond disciplines -> analysis of VABB-SHW data at the author level Method = Cluster analysis 3

Cluster analysis (1) = A multivariate technique for classifying similar objects into groups (authors w/ similar publication patterns = same group) Publication patterns of 1828 established authors (>9 weighted publications, >4 years) 3 attributes (11 variables) – share per author of: Publication type (5) Publication language (2) Co-authorship (1) 4

Cluster Analysis (2) 1.Distance Measure: Mahalanobis distance (Mahalanobis, 1936; Johnson & Wichern, 1992) Matrix of dissimilarity between all authors 2. Principal components analysis + Silhouette: 2-D representation (reduction from 11-D) 2-cluster solution 3. Partitioning algorithm (K-medoids) (Kaufmann & Rousseeuw, 1990) Identification of medoids All objects -> clustered around medoids 5

Publication patterns at higher aggregation levels (Engels et al., 2012) Social Sciences journal-oriented WoS and/or Scopus English frequent co-authorship Humanities importance of book publications national/regional journals Regional language (Dutch) less frequent co-authorship 6

Results 7

Cluster Labels Cluster 1 journal-oriented WoS English frequent collaborations -> Cluster 1= Social Scientists? Cluster 2 importance of book publications national/regional journals Dutch less frequent co-authorship -> Cluster 2 = Humanities scholars? 8

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SS Disciplines in 2 clusters in % Discipline% Cluster 1% Cluster 2 Criminology Economics Educational Sciences Social Health Sciences Sociology Political Sciences Psychology

H Disciplines in 2 clusters in % Discipline% Cluster 1% Cluster 2 Archaeology Communication Sciences History History of Arts Law Linguistics Literature Philosophy Theology

Conclusions Statistics at disciplinary level hide true diversity in SSH research and publishing Sharply contrasting publication patterns of the SS vs. the H is in most cases false Method: cluster analysis is promising for application in research evaluation of the SSH. Identification of: Units to be assessed Outputs to be taken into account 19

Thank you Comments & Questions? 20

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