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Theory Selection in Particle Physics

A Quantitative Case Study of the Evolution of Weak-Electromagnetic Unification Theory

Bibliographic Data

ID11924270
AuthorsDavid Koester (0000-0003-4309-496X, University of Chicago, corresponding author), Daniel J Sullivan (0000-0001-6703-3905, Carleton College), Dan Sullivan (0000-0002-5649-9627, Carleton College, Northfield, Minnesota 55057, USA), D Hywel White (Brookhaven National Laboratory)
Year1982
Volume12
Issue1
Pages73-100
Publication date1982-02-01
Open AccessYes
TypeARTICLE
VenueSocial Studies of Science (JOURNAL)
Journal identifiersISSN: 0306-3127 • E-ISSN: 1460-3659
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/030631282012001004
OpenAlexW2150453528
LanguageEN
Citations received6
References cited7

Drawing a distinction between the micro-level process of theory choice and the macro-level process of theory selection within science, this paper presents an analysis of the development of the weak-electromagnetic unification programme within the specialty of weak interactions in high energy physics. Bibliometric techniques are used to understand the process of theory selection during the rapid interaction of new theoretical and empirical developments. Employing an evolutionary analogy, the interplay of theory and experiment is analyzed during the theory selection process in the context of the history of the specialty. From this perspective, theory and experiment are seen to be closely dependent, as theorists depend heavily on experimental results for both the construction and confirmation of models, while at the same time experimental work depends on theory for understanding the relevance of results

Analogy · Context (archaeology · Epistemology · Macro · Management science · Physics · Process (computing · Relevance (law · Selection (genetic algorithm · Statistical physics · Theoretical physics · Unification · Black Holes and Theoretical Physics · Computer Science · Cosmology and Gravitation Theories · Engineering · Particle physics theoretical and experimental studies · Artificial Intelligence

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Citations per year0.14
Citation span1984 - 2018 (35)
Highly citedNo

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