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S&T education in India: Prospects and challenges

Author

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  • L. P. Rai

    (National Institute of Science Technology and Development Studies)

  • Naresh Kumar

    (National Institute of Science Technology and Development Studies)

Abstract

With the globalisation of the job market, higher education is undergoing structural changes and education scenario worldwide is experiencing dramatic and accelerating changes in patterns of creation of new knowledge. Similar activities are being witnessed in India as regards to the production of highly qualified S&T personnel in different disciplines. In this paper a comparative analysis of doctorates produced in India during 1974 to 1999 in different fields is carried out with the help of mathematical models. Besides analysing the trends of highly qualified S&T personnel with the help of known mathematical models, a few new substitution models have been proposed and applied to explain the movement of researchers from one discipline to the other. Findings suggest that arts, commerce, education and medicine depict growing trends, whereas agriculture, science and veterinary science are traversing a declining path. Further, proposed models are found to be flexible in nature and can capture and explain the shifting patterns very well. These models are comparable to other known models dealing with technology substitution.

Suggested Citation

  • L. P. Rai & Naresh Kumar, 2004. "S&T education in India: Prospects and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(2), pages 157-169, October.
  • Handle: RePEc:spr:scient:v:61:y:2004:i:2:d:10.1023_b:scie.0000041646.78833.37
    DOI: 10.1023/B:SCIE.0000041646.78833.37
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    References listed on IDEAS

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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Joe A. Dodson, Jr. & Eitan Muller, 1978. "Models of New Product Diffusion Through Advertising and Word-of-Mouth," Management Science, INFORMS, vol. 24(15), pages 1568-1578, November.
    3. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
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