How Theories of Induction Can Streamline Measurements of Scientific Performance

We argue that inductive analysis (based on formal learning theory and the use of suitable machine learning reconstructions) and operational (citation metrics-based) assessment of the scientific process can be justifiably and fruitfully brought together, whereby the citation metrics used in the opera...

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Bibliographic Details
Published in:Journal for general philosophy of science 2020-06, Vol.51 (2), p.267-291
Main Authors: Perovic, Slobodan, Sikimic, Vlasta
Format: Article
Language:English
Subjects:
Analysis
Education
History
Inductive reasoning
Learning, Psychology of
Measurement
Methodology of the Social Sciences
Philosophy
Philosophy of Education
Philosophy of Science
Scientific development
Social Sciences
Technology application
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Summary:We argue that inductive analysis (based on formal learning theory and the use of suitable machine learning reconstructions) and operational (citation metrics-based) assessment of the scientific process can be justifiably and fruitfully brought together, whereby the citation metrics used in the operational analysis can effectively track the inductive dynamics and measure the research efficiency. We specify the conditions for the use of such inductive streamlining, demonstrate it in the cases of high energy physics experimentation and phylogenetic research, and propose a test of the method’s applicability.
ISSN:0925-4560
1572-8587
DOI:10.1007/s10838-019-09468-4