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Drawing impossible boundaries: field delineation of Social Network Science
[Zeitschriftenartikel]
Abstract
"Big" digital behavioral data increasingly allows large-scale and high-resolution analyses of the behavior and performance of persons or aggregated identities in whole fields. Often the desired system of study is only a subset of a larger database. The task of drawing a field boundary is complicated... mehr
"Big" digital behavioral data increasingly allows large-scale and high-resolution analyses of the behavior and performance of persons or aggregated identities in whole fields. Often the desired system of study is only a subset of a larger database. The task of drawing a field boundary is complicated because socio-cultural systems are highly overlapping. Here, I propose a sociologically enhanced information retrieval method to delineate fields that is based on the reproductive mechanism of fields, able to account for field heterogeneity, and generally applicable also outside scientometric, e.g., in social media, contexts. The method is demonstrated in a delineation of the multidisciplinary and very heterogeneous Social Network Science field using the Web of Science database. The field consists of 25,760 publications and has a historical dimension (1916-2012). This set has high face validity and exhibits expected statistical properties like systemic growth and power law size distributions. Data is clean and disambiguated. The dataset with 45,580 author names and 23,026 linguistic concepts is publically available and supposed to enable high-quality analyses of an evolving complex socio-cultural system.... weniger
Thesaurusschlagwörter
information retrieval; Scientometrie; Daten; Publikation; soziales Netzwerk; Netzwerkanalyse
Klassifikation
Forschungsarten der Sozialforschung
Freie Schlagwörter
Field delineation; Sociologically enhanced information retrieval; Boundary problem; Social Network Science (SNS); Web of Science
Sprache Dokument
Englisch
Publikationsjahr
2020
Seitenangabe
S. 2841-2876
Zeitschriftentitel
Scientometrics, 125 (2020) 3
DOI
https://doi.org/10.1007/s11192-020-03527-0
ISSN
1588-2861
Status
Veröffentlichungsversion; begutachtet (peer reviewed)