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S&T knowledge production from 2000 to 2009 in two periphery countries: Brazil and South Korea

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Abstract

This paper investigates the dynamic evolution profiles of science and technology knowledge production in Brazil and the Republic of Korea from 2000 to 2009. The two countries have followed different models of publication profiles, bioenvironmental model and Japanese model, and they currently belong to periphery countries in terms of the center-periphery framework. Brazil and the Republic of Korea have established a few core disciplines successfully and increased their share in the world publication of scientific papers over the last decade. Notwithstanding the fact that the two countries have recorded sustained growth in the percentage of published scientific papers, South Korea has evolved into a more balanced science and technology knowledge production system, whereas Brazil into the more unbalanced knowledge production system. Core-lagging or periphery-lagging patterns of science production have been revealed in Brazil and indirectly imply that the existing science base has not been fully stimulated or utilized.

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Notes

  1. Refer to Tables 2 and 3 of Fink et al. (2012).

  2. According to Nagpaul (2003) and Glänzel and Schubert (2005), geographic proximity is an important factor that determines the level of international S&T collaboration.

  3. Table 1 is constructed from Fig. 2 of Chuang et al. (2010, p. 519).

  4. Refer to “Data and research methods” section for the definitions of RCAP and RCAC.

  5. For abbreviations used in Figs. 3 and 4, refer to the Appendix.

  6. For examples of bibliometric methods applied in the evaluation of science-based programs, see http://www.science-metrix.com/.

  7. Refer to the Appendix for the abbreviated arrowhead names in Figs. 6 and 7 and for the full names of scientific fields.

  8. Table 4 contains only 17 fields of science because two small fields, psychology and immunology, are omitted for the sake of simplicity.

References

  • Albuquerque, E. D. M. (2001). Scientific infrastructure and catching-up process: Notes about a relationship illustrated by science and technology statistics. Revista brasileira de economia, 55(4), 545–566.

    Article  Google Scholar 

  • Balassa, B. (1977). ‘Revealed’ comparative advantage revisited: An analysis of relative export shares of the industrial countries, 1953–1971. The Manchester School, 45(4), 327–344.

    Article  Google Scholar 

  • Bound, K. (2008). Brazil, the natural knowledge economy. London: Demos.

    Google Scholar 

  • Choung, J.-Y., & Hwang, H.-R. (2013). The evolutionary patterns of knowledge production in Korea. Scientometrics, 94(2), 629–650.

    Article  Google Scholar 

  • Chuang, Y. W., Lee, L. C., Hung, W. C., & Lin, P. H. (2010). Forging into the innovation lead—A comparative analysis of scientific capacity. International Journal of Innovation Management, 14(03), 511–529.

    Article  Google Scholar 

  • Fink, D., Hameed, T., So, M., Kwon, Y., & Rho, J. J. (2012). S&T collaboration in developing countries: Lessons from Brazilian collaboration activities with South Korea. STI Policy Review, 3, 92–110.

    Google Scholar 

  • Gaillard, J. (2010). Measuring research and development in developing countries: Main characteristics and implications for the Frascati Manual. Science Technology & Society, 15(1), 77–111.

    Article  Google Scholar 

  • Glänzel, W., Debackere, K., & Meyer, M. (2008). ‘Triad’or ‘tetrad’? On global changes in a dynamic world. Scientometrics, 74(1), 71–88.

    Article  Google Scholar 

  • Glänzel, W., Leta, J., & Thijs, B. (2006). Science in Brazil. Part 1: A macro-level comparative study. Scientometrics, 67(1), 67–86.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2005). Analysing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Glänzel, W., Thijs, B., Schubert, A., & Debackere, K. (2009). Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance. Scientometrics, 78(1), 165–188.

    Article  Google Scholar 

  • Hu, X., & Rousseau, R. (2009). A comparative study of the difference in research performance in biomedical fields among selected Western and Asian countries. Scientometrics, 81(2), 475–491.

    Article  Google Scholar 

  • Hwang, K. (2008). International collaboration in multilayered center-periphery in the globalization of science and technology. Science, Technology and Human Values, 33(1), 101–133.

    Article  Google Scholar 

  • Kim, M. J. (2005). Korean science and international collaboration, 1995–2000. Scientometrics, 63(2), 321–339.

    Article  Google Scholar 

  • Kwon, K. S. (2011). The co-evolution of universities’ academic research and knowledge-transfer activities: the case of South Korea. Science and Public Policy, 38(6), 493–503.

    Article  Google Scholar 

  • Larivière, V. (2010). A bibliometric analysis of Quebec’s PhD students’ contribution to the advancement of knowledge. Doctoral dissertation, McGill University

  • Lattimore, R., & Revesz, J. (1996). Australian science: Performance from published papers. Australian Government Pub. Service. http://www.pc.gov.au/bureau-industry-economics/report/96-03. Accessed 15 October 2012.

  • Leta, J., Glänzel, W., & Thijs, B. (2006). Science in Brazil. Part 2: Sectoral and institutional research profiles. Scientometrics, 67(1), 87–105.

    Article  Google Scholar 

  • Nagpaul, P. S. (2003). Exploring a pseudo-regression model of transnational cooperation in science. Scientometrics, 56(3), 403–416.

    Article  Google Scholar 

  • Schubert, A., & Braun, T. (1986). Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics, 9(5), 281–291.

    Article  Google Scholar 

  • Schulz, P. A., & Manganote, E. J. (2012). Revisiting country research profiles: learning about the scientific cultures. Scientometrics, 1–15.

  • Widgrén, M. (2005). Revealed comparative advantage in the Internal Market (No. 989). ETLA Discussion Papers. The Research Institute of the Finnish Economy (ETLA). http://hdl.handle.net/10419/63731 Accessed 2 November 2012.

  • Yi, Y., Qi, W., & Wu, D. (2013). Are CIVETS the next BRICs? A comparative analysis from scientometrics perspective. Scientometrics, 94(2), 615–628.

    Article  Google Scholar 

  • Zelnio, R. (2012). Identifying the global core-periphery structure of science. Scientometrics, 91(2), 601–615.

    Article  Google Scholar 

  • Zhou, P., Thijs, B., & Glänzel, W. (2009). Regional analysis on Chinese scientific output. Scientometrics, 81(3), 839–857.

    Article  Google Scholar 

  • Zitt, M., & Bassecoulard, E. (2008). Challenges for scientometric indicators: Data demining, knowledge-flow measurements and diversity issues. Ethics in science and environmental politics, 8(5–7), 49–60.

    Article  Google Scholar 

  • Zitt, M., Bassecoulard, E., & Okubo, Y. (2000). Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science. Scientometrics, 47(3), 627–657.

    Article  Google Scholar 

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Correspondence to Youngsun Kwon.

Appendix

Appendix

NSI fields and abbreviations

Abbreviation

Field

Abbreviation

Field

AGRI

Agricultural sciences

MAT

Mathematics

BIO

Biology and biochemistry

MIC

Microbiology

CHE

Chemistry

MOL

Molecular biology and genetics

MED

Clinical medicine

NEU

Neuroscience and behavior

CS

Computer science

PHA

Pharmacology and toxicology

ENG

Engineering

PHY

Physics

ENV

Environment/ecology

ANI

Plant and animal science

GEO

Geosciences

PSY

Psychiatry/psychology

IMM

Immunology

SPA

Space science

MS

Materials science

  

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Fink, D., Kwon, Y., Rho, J.J. et al. S&T knowledge production from 2000 to 2009 in two periphery countries: Brazil and South Korea. Scientometrics 99, 37–54 (2014). https://doi.org/10.1007/s11192-013-1085-6

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