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2篇 您的检索式：作者名="Lawrence Smolinsky"

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		题名	作者	年代	出处	被引量
	1	Co-author Weighting in Bibliometric Methodology and Subfields of a Scientific Discipline显示文摘Purpose: To give a theoretical framework to measure the relative impact of bibliometric methodology on the subfields of a scientific discipline, and how that impact depends on the method of evaluation used to credit individual scientists with citations and publications. The authors include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors.Design/methodology/approach: The theoretical methodology introduces the notion of credit spaces for a discipline. These quantify the total citation or publication credit accumulated by the scientists in the discipline. One can then examine how the credit is divided among the subfields. The design of the physics study uses the American Physical Society print journals to assign subdiscipline classifications to articles and gather citation, publication, and author information. Credit spaces for the collection of Physical Review Journal articles are computed as a proxy for physics.Findings: There is a substantial difference in the value or impact of a specific subfield depending on the credit system employed to credit individual authors.Research limitations: Subfield classification information is difficult to obtain. In the illustrative physics study, subfields are treated in groups designated by the Physical Review journals. While this collection of articles represents a broad part of the physics literature, it is not all the literature nor a random sample.Practical implications: The method of crediting individual scientists has consequences beyond the individual and affects the perceived impact of whole subfields and institutions. Originality/value: The article reveals the consequences of bibliometric methodology on subfields of a disciple by introducing a systematic theoretical framework for measuring the consequences.	Lawrence Smolinsky Aaron J Lercher	2020	Journal of Data and Information Science2020，5，3：	3
	2	Confidence Intervals for Relative Intensity of Collaboration(RIC)Indicators显示文摘Purpose:We aim to extend our investigations related to the Relative Intensity of Collaboration(RIC)indicator,by constructing a confidence interval for the obtained values.Design/methodology/approach:We use Mantel-Haenszel statistics as applied recently by Smolinsky,Klingenberg,and Marx.Findings:We obtain confidence intervals for the RIC indicatorResearch limitations:It is not obvious that data obtained from the Web of Science(or any other database)can be considered a random sample.Practical implications:We explain how to calculate confidence intervals.Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement.Our approach presents a suggestion to solve this problem.Originality/value:Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.	Joel Emanuel Fuchs Lawrence Smolinsky Ronald Rousseau	2022	Journal of Data and Information Science2022，7，4：	0

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