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Using text analysis to quantify the similarity and evolution of scientific disciplines

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20 M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classific...

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Detalles Bibliográficos
Autores principales: Dias, Laércio, Gerlach, Martin, Scharloth, Joachim, Altmann, Eduardo G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792934/
https://www.ncbi.nlm.nih.gov/pubmed/29410857
http://dx.doi.org/10.1098/rsos.171545
Descripción
Sumario:We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20 M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g. computer science) are becoming increasingly central, but that on average the similarity between pairs of disciplines has not changed in the last decades. This suggests that tendencies of convergence (e.g. multi-disciplinarity) and divergence (e.g. specialization) of disciplines are in balance.