Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases

Pech, Gerson; Delgado, Catarina

doi:10.1007/s11192-020-03386-9

Cited by 4 publications

(1 citation statement)

References 64 publications

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“…The same is true for the “Physics, Applied” WoSSC—which, for example, contains papers of Nuclear and Condensed , two other subfields with different research trends. That is, our classification results can provide better support for the calculation of normalized citation metrics within each subfield, an issue of growing interest in Scientometrics (Pech & Delgado, 2020, 2021); consequently, they provide more accurate and reliable academic performance evaluations of researchers and institutions—for financial, strategic or Human Resource management purposes.…”

Section: Discussionmentioning

confidence: 93%

Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics

Pech

Delgado

Sorella

2022

Asso for Info Science & Tech

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Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These “exclusive journals” are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.

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Section: Discussionmentioning

confidence: 93%