How Theories of Induction Can Streamline Measurements of Scientific Performance

Slobodan Perović; Vlasta Sikimić

doi:10.1007/s10838-019-09468-4

How Theories of Induction Can Streamline Measurements of Scientific Performance

Slobodan Perović (Corresponding author), Vlasta Sikimić

Research output: Contribution to journal › Article › Academic › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	267-291
Number of pages	25
Journal	Journal for General Philosophy of Science
Volume	51
Issue number	2
DOIs	https://doi.org/10.1007/s10838-019-09468-4
Publication status	Published - 1 Jun 2020
Externally published	Yes

Funding

This work was presented at the conference “Formal Methods of Scientific Inquiry” held at the Ruhr-University, Bochum in 2017. We are greatful to the participants of the conference, audience at the Center for Formal Epistemology at the Carnegie Mellon University, Kevin T. Kelly, Oliver Schulte, Konstantine (Casey) Genin, anonymous referees and guest editors of the special issue for a number of comments and constructive criticisms. This work was supported by grant #179041 of the Ministry of Education, Science, and Technological Development of the Republic of Serbia. This work was presented at the conference ?Formal Methods of Scientific Inquiry? held at the Ruhr-University, Bochum in 2017. We are greatful to the participants of the conference, audience at the Center for Formal Epistemology at the Carnegie Mellon University, Kevin T. Kelly, Oliver Schulte, Konstantine (Casey) Genin, anonymous referees and guest editors of the special issue for a number of comments and constructive criticisms. This work was supported by grant #179041 of the Ministry of Education, Science, and Technological Development of the Republic of Serbia.

Keywords

Bibliometrics
Formal learning theory
High energy physics
Induction
Phylogenetics
Scientometrics

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10.1007/s10838-019-09468-4

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How Theories of Induction Can Streamline Measurements of Scientific Performance

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