How Theories of Induction Can Streamline Measurements of Scientific Performance
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.
Keywords:
Scientometrics / Phylogenetics / Induction / High energy physics / Formal learning theory / BibliometricsSource:
Journal for General Philosophy of Science, 2020, 51, 2, 267-291Publisher:
- Springer, Dordrecht
Funding / projects:
- Dynamic Systems in Nature and Society: Philosophical and Empirical Aspects (RS-MESTD-Basic Research (BR or ON)-179041)
DOI: 10.1007/s10838-019-09468-4
ISSN: 0925-4560
WoS: 000535137500004
Scopus: 2-s2.0-85070295080
Institution/Community
Filozofija / PhilosophyTY - JOUR AU - Perović, Slobodan AU - Sikimić, Vlasta PY - 2020 UR - http://reff.f.bg.ac.rs/handle/123456789/3162 AB - 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. PB - Springer, Dordrecht T2 - Journal for General Philosophy of Science T1 - How Theories of Induction Can Streamline Measurements of Scientific Performance EP - 291 IS - 2 SP - 267 VL - 51 DO - 10.1007/s10838-019-09468-4 ER -
@article{ author = "Perović, Slobodan and Sikimić, Vlasta", year = "2020", 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.", publisher = "Springer, Dordrecht", journal = "Journal for General Philosophy of Science", title = "How Theories of Induction Can Streamline Measurements of Scientific Performance", pages = "291-267", number = "2", volume = "51", doi = "10.1007/s10838-019-09468-4" }
Perović, S.,& Sikimić, V.. (2020). How Theories of Induction Can Streamline Measurements of Scientific Performance. in Journal for General Philosophy of Science Springer, Dordrecht., 51(2), 267-291. https://doi.org/10.1007/s10838-019-09468-4
Perović S, Sikimić V. How Theories of Induction Can Streamline Measurements of Scientific Performance. in Journal for General Philosophy of Science. 2020;51(2):267-291. doi:10.1007/s10838-019-09468-4 .
Perović, Slobodan, Sikimić, Vlasta, "How Theories of Induction Can Streamline Measurements of Scientific Performance" in Journal for General Philosophy of Science, 51, no. 2 (2020):267-291, https://doi.org/10.1007/s10838-019-09468-4 . .