Skewed distributions of scientists’ productivity: a research program for the empirical analysis
Abstract
Only a few scientists are able to publish a substantial number of papers every year; most of the scientists have an output of only a few publications or no publications at all. Several theories (e.g., the “sacred spark” theory) have been proposed in the past to explain these productivity differences that are complementary and focus on different aspects in the publication process. This study is intended to introduce a research program for studying productivity differences in science (skewed distributions of scientists’ productivity). The program is based on the Anna Karenina Principle (AKP). The AKP states that success in research is the result of several prerequisites that are multiplicatively related. Great success results from prerequisites that must be all given. If at least one prerequisite is not given, failure follows, whereby the failure is specific to the set of given and missing prerequisites. High productivity is given for the few scientists who fulfill all prerequisites (e.g., high motivation, pronounced creativity, reputational professional position, early important papers in high-impact journals), and low productivity is connected to a specific combination of missing and fulfilled prerequisites for many scientists. Besides the AKP as theoretical principle, the program for studying productivity differences includes a mathematical concept explaining skewed distributions and statistical methods for empirical productivity analyses.
References
[1]
Abramo G, D’Angelo CA, and Di Costa F Testing the trade-off between productivity and quality in research activities Journal of the American Society for Information Science and Technology 2010 61 1 132-140
[2]
Abramo G, D’Angelo CA, and Soldatenkova A How long do top scientists maintain their stardom? An analysis by region, gender and discipline: Evidence from Italy Scientometrics 2017 110 2 867-877
[3]
Bornmann L and Daniel H-D Criteria used by a peer review committee for selection of research fellows: A Boolean probit analysis International Journal of Selection and Assessment 2005 13 4 296-303
[4]
Bornmann L, Haunschild R, and Mutz R Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases Humanities and Social Sciences Communications 2021 8 1 224
[5]
Bornmann L and Marx W The Anna Karenina principle: A way of thinking about success in science Journal of the American Society for Information Science and Technology 2012 63 10 2037-2051
[6]
Bornmann L and Mutz R Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references Journal of the Association for Information Science and Technology 2015 66 11 2215-2222
[7]
Brand J and Hardy R Editorial commentary: Causes of patient dissatisfaction may be modifiable Arthroscopy: The Journal of Arthroscopic & Related Surgery 2022 38 12 3207-3208
[8]
Braumoeller BF Causal complexity and the study of politics Political Analysis 2003 11 3 209-233
[9]
Braumoeller BF Boolean logit and probit in Stata The Stata Journal 2004 4 4 436-441
[10]
Cole JR and Cole S Social stratification in science 1973 The University of Chicago Press
[11]
Costas R, Bordons M, van Leeuwen TN, and van Raan AFJ Scaling rules in the science system: Influence of field-specific citation characteristics on the impact of individual researchers Journal of the American Society for Information Science and Technology 2009 60 4 740-753
[12]
Diamond J Zebras and the Anna Karenina principle Natural History 1994 103 9 4-10
[13]
Diamond, J. M. (1997). Guns, germs, and steel: the fates of human societies. W. W. Norton.
[14]
Diem A and Wolter SC The use of bibliometrics to measure research performance in education sciences Research in Higher Education 2013 54 1 86-114
[15]
Dong, Y., Ma, H., Shen, Z., & Wang, K. (2017). A century of science: Globalization of scientific collaborations, citations, and innovations. Paper presented at the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery.
[16]
Gilbride TJ and Allenby GM A choice model with conjunctive, disjunctive, and compensatory screening rules Marketing Science 2004 23 3 391-406
[17]
Hammarfelt B, Rushforth A, and de Rijcke S Temporality in academic evaluation: ‘Trajectoral thinking’ in the assessment of biomedical researchers Valuation Studies 2020 7 33
[18]
Haslam N and Laham SM Quality, quantity, and impact in academic publication European Journal of Social Psychology 2010 40 2 216-220
[19]
Hemlin S Research on research evaluations Social Epistemology 1996 10 2 209-250
[20]
Ioannidis JPA, Boyack KW, and Klavans R Estimates of the continuously publishing core in the scientific workforce PLoS ONE 2014 9 7 e101698
[21]
Ioannidis JPA, Klavans R, and Boyack KW The scientists who publish a paper every five days Nature 2018 561 7722 167-169
[22]
Kroneberg, C. (2012). The rescue of Jews in WWII: An action-theoretic and empirical analysis. Kölner Zeitschrift Für Soziologie Und Sozialpsychologie,64(1), 37–65.
[23]
Kwiek M The European research elite: A cross-national study of highly productive academics in 11 countries Higher Education 2015 71 3 379-397
[24]
Larivière V and Costas R Salah AA, Tonta Y, Salah AAA, Sugimoto C, and Al U How many is too many? On the relationship between output and impact in research The 15th Conference of the International Society for Scientometrics and Informetrics 2015 ISSI, Boaziçi University Printhouse 590-595
[25]
Larivière V and Costas R How many is too many? On the relationship between research productivity and impact PLoS ONE 2016 11 9 e0162709
[26]
Lee DH Predicting the research performance of early career scientists Scientometrics 2019 121 3 1481-1504
[27]
Li J, Yin Y, Fortunato S, and Wang D Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact Journal of the Royal Society Interface 2020 17 165 20200135
[28]
Li, W. H., Aste, T., Caccioli, F., & Livan, G. (2019). Early coauthorship with top scientists predicts success in academic careers. Nature Communications.
[29]
Lotka AJ The frequency distribution of scientific productivity Journal of the Washington Academy of Sciences 1926 12 317-323
[30]
Marx W and Bornmann L How accurately does Thomas Kuhn’s model of paradigm change describe the transition from a static to a dynamic universe in cosmology? A historical reconstruction and citation analysis Scientometrics 2010 84 2 441-464
[31]
Marx W and Bornmann L The emergence of plate tectonics and the Kuhnian model of paradigm shift: A bibliometric case study based on the Anna Karenina principle Scientometrics 2013 94 2 595-614
[32]
McClay AS and Balciunas JK The role of pre-release efficacy assessment in selecting classical biological control agents for weeds: Applying the Anna Karenina principle Biological Control 2005 35 3 197-207
[33]
Milojevic S, Radicchi F, and Walsh JP Changing demographics of scientific careers: The rise of the temporary workforce Proceedings of the National Academy of Sciences of the United States of America 2018 115 50 12616-12623
[34]
Moore DRJ The Anna Karenina principle applied to ecological risk assessments of multiple stressors Human and Ecological Risk Assessment 2001 7 2 231-237
[35]
Nicholls PT Price’s square root law: Empirical validity and relation to Lotka’s law Information Processing & Management 1988 24 4 469-477
[36]
Nielsen MW and Andersen JP Global Citation Inequality is on the Rise Proceedings of the National Academy of Sciences 2021 118 7 e2012208118
[37]
Park M, Leahey E, and Funk RJ Papers and patents are becoming less disruptive over time Nature 2023 613 138-144
[38]
Popper KR Objective knowledge: An evolutionary approach 1972 Clarendon Press
[39]
Price DJDS Little science, big science 1963 Columbia University Press
[40]
Rescher N Scientific progress: A philosophical essay on the economics of research in natural science 1978 Blackwell’s
[41]
Seglen PO The skewness of science Journal of the American Society for Information Science 1992 43 9 628-638
[42]
Shockley W On the statistics of individual variations of productivity in research laboratories Proceedings of the Institute of Radio Engineers 1957 45 3 279-290
[43]
Shugan SM The Anna Karenina bias: Which variables to observe? Marketing Science 2007 26 2 145-148
[44]
Shugan SM and Mitra D Metrics: When and why nonaveraging statistics work Management Science 2009 55 1 4-15
[45]
Sinatra R, Wang D, Deville P, Song C, and Barabási A-L Quantifying the evolution of individual scientific impact Science 2016 354 6312 aaf5239
[46]
Sonnert G What makes a good scientist? Determinants of peer evaluation among biologists Social Studies of Science 1995 25 1 35-55
[47]
Tabah AN Literature dynamics: Studies on growth, diffusion, and epidemics Annual Review of Information Science and Technology 1999 34 249-286
[48]
Tahamtan I and Bornmann L Core elements in the process of citing publications: Conceptual overview of the literature Journal of Informetrics 2018 12 1 203-216
[49]
Tahamtan I and Bornmann L What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018 Scientometrics 2019 121 3 1635-1684
[50]
Tolstoy, L. (1875-1877/2001). Anna Karenina (Original work published 1875-1877) (R. P. L. Volokhonsky, Trans.). Viking Penguin
[51]
van Raan AFJ Advanced bibliometric methods for the evaluation of universities Scientometrics 1999 45 3 417-423
[52]
van Raan AFJ Glänzel W, Moed HF, Schmoch U, and Thelwall M Measuring science: Basic principles and application of advanced bibliometrics Springer handbook of science and technology indicators 2019 Springer International Publishing 237-280
[53]
Wang D and Barabási A-L The science of science 2021 Cambridge University Press
[54]
Weingartner S Multiple paths to the opera? The social structure of decision processes in cultural consumption Kölner Zeitschrift Für Soziologie Und Sozialpsychologie 2019 71 1 53-79
[55]
Zalewska-Kurek K, Geurts PATM, and Roosendaal HE The impact of the autonomy and interdependence of individual researchers on their production of knowledge and its impact: An empirical study of a nanotechnology institute Research Evaluation 2010 19 3 217-225
Recommendations
Scientometric analysis of Iraqi-Kurdistan universities' scientific productivity
Purpose - This purpose of this study is to examine research performance of Iraqi-Kurdistan universities, using the number of papers appearing in journals and proceedings, and the number of citations received by those papers as covered by Scopus, 1970-...
Meta-analysis in psychology: a bibliometric study
Meta-analysis refers to the statistical methods used in research synthesis for combining and integrating results from individual studies. The present study draws on the strengths of bibliometric methods in order to offer an overview of meta-analytic ...
Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute
This study aims to undertake a bibliometric investigation of the NASA Astrobiology Institute (NAI) funded research that was published between 2008 and 2012 (by teams of Cooperative Agreement Notice Four and Five). For this purpose, the study creates an ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s) 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 March 2024
Accepted: 06 February 2024
Received: 28 October 2023
Author Tags
Qualifiers
- Research-article
Funding Sources
- Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (2)
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Nov 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in