How Theories of Induction Can Streamline Measurements of Scientific Performance
Само за регистроване кориснике
2020
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Scientometrics / Phylogenetics / Induction / High energy physics / Formal learning theory / BibliometricsИзвор:
Journal for General Philosophy of Science, 2020, 51, 2, 267-291Издавач:
- Springer, Dordrecht
Финансирање / пројекти:
- Динамички системи у природи и друштву: Филозофски и емпиријски аспекти (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
Институција/група
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 . .