Abstract
This paper investigates the social space of physics research institutions. Scientific capital is a well-known concept for measuring and assessing the accumulated recognition and the specific scientific power developed by Pierre Bourdieu. The scientific capital of a physics research institution manifests itself as a reputation, a high-profile name in the field of physics, symbols of academic recognition, and scientific status. Using citation statistics from the Web of Science Core Collection and sociological data of dedicated survey “The Monitoring of the Labor Market for Highly Qualified R&D Personnel” we construct the social space of Russian physics institutions. The analysis reveals generalized grounds of social space of Russian physics institutions: principles of visibility and scientific capital. The study highlights internal differentiation of physics institutions on three groups (“major”, “high energy”, and “secondary” institutions). The social space of physics research institutions provides a map of field of physics in Russia. This research may be a useful starting point for developing a more comprehensive study of the field of physics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Archer, L., Dawson, E., DeWitt, J., Seakins, A., & Wong, B. (2015). “Science capital”: A conceptual, methodological, and empirical argument for extending Bourdieusian notions of capital beyond the arts. Journal of Research in Science Teaching, 52(7), 922–948. doi:10.1002/tea.21227.
Auriol, L. (2007). PhD holders: The labor market and international mobility. Foresight–Russia, 1(3), 34–48. http://foresight-journal.hse.ru/en/2007-1-3/26558538.html.
Auriol, L. (2014). Careers of doctorate holders: Employment and mobility patterns. OECD Science, Technology and Industry Working Papers 4, OECD, Paris. doi:10.1787/5kmh8phxvvf5-en.
Auriol, L., Misu, M., & Freeman, R. (2013). Doctorate holders: Labour market and mobility indicators. Foresight–Russia, 7(4). 16–42. http://foresight-journal.hse.ru/en/2013-7-4/107116768.html.
Bak, P. (1996). How nature works: The science of self-organized criticality. New York: Copernicus. doi:10.1007/978-1-4757-5426-1.
Bellotti, E. (2011). The social processes of production and validation of knowledge in particle physics: Preliminary theoretical and methodological observations. Procedia—Social and Behavioral Sciences, 10, 148–159. doi:10.1016/j.sbspro.2011.01.018.
Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling: Theory and applications (2nd ed.)., Springer series in statistics New York: Springer. doi:10.1007/0-387-28981-X.
Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80. doi:10.1108/00220410810844150.
Bourdieu, P. (1975). The specificity of the scientific field and the social conditions of the progress of reason. Social Science Information, 14(6), 19–47. doi:10.1177/053901847501400602.
Bourdieu, P. (1985). The social space and the genesis of groups. Theory and Society, 14(6), 723–744. doi:10.1007/BF00174048.
Bourdieu, P. (2002). The forms of capital. In N. W. Biggart (Ed.), Readings in economic sociology (pp. 280–291). Malden, MA: Blackwell. doi:10.1002/9780470755679.ch15.
Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Cambridge, MA: Harvard University Press. http://www.hup.harvard.edu/catalog.php?isbn=9780674212770.
Bourdieu, P. (1988). Homo Academicus. Stanford, CA: Stanford University Press. http://www.sup.org/books/title/?id=2475.
Bourdieu, P. (1997). Les Usages Sociaux de la Science: Pour une Sociologie Clinique du Champ Scientifique. Les Éditions INRA, Paris. http://www.quae.com/fr/r480-les-usages-sociaux-de-la-science.html.
Bourdieu, P. (2004). Science of science and reflexivity. Chicago, IL: University of Chicago Press. http://press.uchicago.edu/ucp/books/book/chicago/S/bo3630402.html.
Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press. http://press.uchicago.edu/ucp/books/book/chicago/I/bo3649674.html.
Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: Implications for scientific and technical human capital. Research Policy, 33(4), 599–616. doi:10.1016/j.respol.2004.01.008.
Bozeman, B., Dietz, J. S., & Gaughan, M. (2001). Scientific and technical human capital: An alternative model for research evaluation. International Journal of Technology Management, 22(7/8), 716–740. doi:10.1504/IJTM.2001.002988.
Brosnan, C. (2011). The significance of scientific capital in UK medical education. Minerva, 49, 317–332. doi:10.1007/s11024-011-9177-z.
Brubaker, R. (2005). Rethinking classical theory. In D. Swartz & V. Zolberg (Eds.), After Bourdieu: Influence, critique, elaboration (pp. 25–64). Boston, MA: Springer. doi:10.1007/1-4020-2589-0-3.
Calhoun, C. (1993). Habitus, field, and capital: The question of historical specificity. In: C. Calhoun, E. LiPuma,&, M. Postone (Eds.), Bourdieu: Critical perspectives (pp 61–88). Chicago, IL: University of Chicago Press. http://eprints.lse.ac.uk/42383/.
Camic, C. (2011). Bourdieu’s cleft sociology of science. Minerva, 49, 275–293. doi:10.1007/s11024-011-9176-0.
Castelvecchi, D. (2015). Physics paper sets record with more than 5,000 authors. Nature,. doi:10.1038/nature.2015.17567.
Cole, J. R., & Cole, S. (1981). Social stratification in science. Chicago, IL: University of Chicago Press.
Coradini, O. L. (2010). The divergences between Bourdieu’s and Coleman’s notions of social capital and their epistemological limits. Social Science Information, 49(4), 563–583. doi:10.1177/0539018410377130.
Corolleur, C. D., Carrere, M., & Mangematin, V. (2004). Turning scientific and technological human capital into economic capital: The experience of biotech start-ups in France. Research Policy, 33(4), 631–642. doi:10.1016/j.respol.2004.01.009.
Cronin, B. (2001). Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices? Journal of the American Society for Information Science and Technology, 52(7), 558–569. doi:10.1002/asi.1097.
Dietz, J. S., & Bozeman, B. (2005). Academic careers, patents, and productivity: Industry experience as scientific and technical human capital. Research Policy, 34(3), 349–367. doi:10.1016/j.respol.2005.01.008.
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Hierarchical clustering. Chichester: Wiley. doi:10.1002/9780470977811.ch4.
Garforth, L., & Kerr, A. (2011). Interdisciplinarity and the social sciences: Capital, institutions and autonomy. The British Journal of Sociology, 62(4), 657–676. doi:10.1111/j.1468-4446.2011.01385.x.
Gläser, J., & Laudel, G. (2001). Integrating scientometric indicators into sociological studies: Methodical and methodological problems. Scientometrics, 52(3), 411–434. doi:10.1023/A:1014243832084.
Gläser, J., & Laudel, G. (2007). The social construction of bibliometric evaluations. In R. Whitley & J. Gläser (Eds.), The changing governance of the sciences: The advent of research evaluation systems (pp. 101–123). Dordrecht: Springer. doi:10.1007/978-1-4020-6746-4_5.
Grenfell, M. (2012). Pierre Bourdieu: Key concepts (2nd ed.). Stockswell: Acumen Publishing. https://www.routledge.com/products/9781844655304.
Hong, W. (2008). Domination in a scientific field: Capital struggle in a Chinese isotope lab. Social Studies of Science, 38, 543–570. doi:10.1177/0306312706092456.
Hu, X., Rousseau, R., & Chen, J. (2010). In those fields where multiple authorship is the rule, the \(h\)-index should be supplemented by role-based \(h\)-indices. Journal of Information Science, 36(1), 73–85. doi:10.1177/0165551509348133.
Katchanov, Y. L., & Shmatko, N. A. (2014). Complexity-based modeling of scientific capital: An outline of mathematical theory. International Journal of Mathematics and Mathematical Sciences. doi:10.1155/2014/785058.
Lebaron, F. (2001). Economists and the economic order: The field of economists and the field of power in France. European Societies, 3(1), 91–110. doi:10.1080/14616690120046969.
Lebaron, F. (2003). Dispositions, social structures and economic practices: Towards a new economic sociology. In: E. Fullbrook (Ed.), Intersubjectivity in economics: Agents and structures (pp. 231–240). London: Routledge. http://www.taylorandfrancis.com/books/details/9780415266987/.
Lebaron, F. (2009). How Bourdieu “quantified” Bourdieu: The geometric modelling of data. In K. Robson & C. Sanders (Eds.), Quantifying theory: Pierre Bourdieu (pp. 11–29). Dordrecht: Springer. doi:10.1007/978-1-4020-9450-7_2.
Lebaron, F., & Grenfell, M. (Eds.). (2014). Bourdieu and data analysis. Methodological principles and practice. Peter Lang AG, Internationaler Verlag der Wissenschaften, Oxford, Bern, Berlin, Bruxelles, Frankfurt am Main, New York, Wien. http://www.peterlang.com/index.cfm?event=cmp.ccc.seitenstruktur.detailseiten&seitentyp=produkt&pk=68922.
Leydesdorff, L. (1998). Theories of citation? Scientometrics, 43(1), 5–25. doi:10.1007/BF02458391.
Lin, M. W., & Bozeman, B. (2006). Researchers’ industry experience and productivity in university-industry research centers: A “scientific and technical human capital” explanation. The Journal of Technology Transfer, 31, 269–290. doi:10.1007/s10961-005-6111-2.
McGuire, W. L. (2011). Constructing quality in academic science: How basic scientists respond to canadian market-oriented science policy—A Bourdieusian approach. Ph.D. thesis, Dalla Lana School of Public Health, University of Toronto, Toronto. http://hdl.handle.net/1807/31862.
Must, U. (2014). The impact of multi-authored papers: The case of a small country. Collnet Journal of Scientometrics and Information Management, 8(1), 41–47. doi:10.1080/09737766.2014.916874.
Panofsky, A. (2011). Field analysis and interdisciplinary science: Scientific capital exchange in behavior genetics. Minerva, 49, 295–316. doi:10.1007/s11024-011-9175-1.
Pritychenko, B. (2015). Intriguing trends in nuclear physics authorship. Scientometrics, 105(3), 1781–1786. doi:10.1007/s11192-015-1605-7.
Ruget, V. (2002). Scientific capital in American political science: Who possesses what, when and how? New Political Science, 24(3), 469–478. doi:10.1080/0739314022000005464.
Sidhu, R., Yeoh, B., & Chang, S. (2014). A situated analysis of global knowledge networks: Capital accumulation strategies of transnationally mobile scientists in Singapore. Higher Education, 69(1), 79–101. doi:10.1007/s10734-014-9762-9.
Sismondo, S. (2011). Bourdieu’s rationalist science of science: Some promises and limitations. Cultural Sociology, 5(1), 83–97. doi:10.1177/1749975510389728.
Small, H. (1998). Citations and consilience in science. Scientometrics, 43(1), 143–148. doi:10.1007/BF02458403.
Takane, Y., Young, F. W., & de Leeuw, J. (1977). Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. Psychometrika, 42(1), 7–67. doi:10.1007/BF02293745.
Wacquant, L. (2008). Pierre Bourdieu. In R. Stones (Ed.), Key sociological thinkers (pp. 261–277). Houndmills: Palgrave Macmillan. doi:10.1007/978-1-349-26616-6_17.
Zuckerman, H. (1996). Scientific elite: Nobel laureates in the United States. New Brunswick, NJ: Transaction Publishers. http://www.transactionpub.com/title/Scientific-Elite-978-1-56000-855-2.html.
Acknowledgments
The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5-100”.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Description of the sample the scientists design
The target population included persons aged from 25 to 69 years who live and work in Russia and have doctoral degrees. In this survey, multistep stratified sampling was used with quotas under the following parameters: gender, age, field of science, employment sector and geographical area. The nationally representative sample was clustered within eight Russian Federal districts and stratified by the number of Ph.D. graduates in each district. The sample was about the same in 2010 (3450 persons) and in 2013 (3492 persons). In 2013, the selected population was comprised of 1914 men (54.8 %) and 1578 women (45.2 %) who were employed at research institutions, universities and R&D organizations and represented all fields of science and engineering. Individual on-the-job interviewing was used.
Appendix 2: Variables related to SC
-
1.
“Symbolic power”—the active properties that provide the respondent with the ability to apportion other signs of scientific recognition:
-
(1)
biography published in the Russian encyclopedia/handbook
-
(2)
biography published in the international/foreign Encyclopedia/handbook
-
(3)
public conference/talk in Russia
-
(4)
public conference/talk in foreign countries
-
(5)
publications in the media
-
(6)
speech on the radio or on television
-
(7)
publications about him/her in the media (interviews, reviews, etc.)
-
(8)
personal blog or site on the Internet
-
(9)
citation index
-
(10)
number of peer-reviewed articles in leading Russian journals
-
(11)
number of peer-reviewed articles in leading International journals (Web of Science, Scopus, etc.)
-
(12)
monographs in a national publisher house
-
(13)
monographs in a foreign publisher house
-
(14)
translations of his or her work into foreign languages
-
(15)
patents
-
(16)
scientific and academic awards from Russia and other countries
-
(17)
personal grants received
-
(18)
number of the foreign languages used by respondent in professional communication (reading literature, presentations or lectures, writing papers)
-
(1)
-
2.
“Bureaucratic power”—the active properties that allow the respondent access to institutional resources:
-
(19)
participation in scientific councils
-
(20)
membership on editorial boards
-
(21)
membership in governmental/national expert boarding/council
-
(22)
membership in committee on graduate programs for graduate theses
-
(23)
assignment to administrative posts connected with the distribution of employment and financial resources
-
(24)
administrative posts connected with management of national and international scientific and educational projects
-
(25)
leading position at university/research institution
-
(19)
-
3.
“Academic power”—the active properties that enable control of the social reproduction of the corps of scientists:
-
(26)
membership in professional organizations/associations
-
(27)
membership in governmental/national expert boarding/council
-
(28)
membership in thesis/dissertation examining committee
-
(29)
supervision of dissertations
-
(30)
number of doctorate awarded under his/her supervision
-
(26)
-
4.
Post-graduate training/retraining:
-
(31)
courses, trainings, seminars in own or related areas
-
(32)
courses, trainings, seminars in other areas of specialization
-
(33)
courses, trainings, workshops in management, planning, etc.
-
(34)
computer courses in certain software products
-
(35)
foreign languages courses
-
(31)
Appendix 3: The variables used when constructing the social space of Russian PI
-
Variables \(\#\)1–6. Number of publications in 2008–2013
-
Variables \(\#\)7–12. Number of citations to papers published in 2008–2013
-
Variable \(\#\)13. Share of Russian institutions, i.e. the average share of Russian organizations in the total number of organizations affiliated with the publications of an institution and published in 2008–2013
-
Variable \(\#\)14. Number of scientific personnel in 2008
-
Variable \(\#\)15. Number of highly cited authors (stars)
-
Variable \(\#\)16. Total number of citations to publications of highly cited authors (starting from 1986)
-
Variable \(\#\)17. Average number of citations to papers published by one highly cited author (starting from 1986)
-
Variable \(\#\)18. Total number of citations to publications of highly cited authors (over the last 7 years)
-
Variable \(\#\)19. Average number of citations to papers published by one highly cited author (over the last 7 years)
-
Variable \(\#\)20. Scientific capital of physics institution (SCI)
Notes:
-
1.
Information on publications and citations (variables \(\#\) 1–13) was extracted from the database Web of Science. Accessed: June 2014.
-
2.
Data for variable \(\#\) 14 were extracted from the website of the Russian Academy of Sciences. Source: http://www.ras.ru/presidium/documents/directions.aspx?ID=07f28cf4-5660-46a3-abab-e18dd3771026. Accessed: April 2015.
-
3.
Data for variables \(\#\) 15–19 were extracted from the “Expert Corpus”. Source: http://expertcorps.ru/science/whoiswho/affs. Accessed: April 2015.
Appendix 4: Russian PI in the sample
See Table 1.
Rights and permissions
About this article
Cite this article
Katchanov, Y.L., Markova, Y.V. & Shmatko, N.A. How physics works: scientific capital in the space of physics institutions. Scientometrics 108, 875–893 (2016). https://doi.org/10.1007/s11192-016-2005-3
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-016-2005-3