Science policymakers and research evaluators are increasingly focusing on alternative methods of assessing the public investment in science and engineering research. Over the course of the last 20 years, scientific and engineering research centers with ties to industry have become a permanent fixture of the academic research landscape. Yet, much of the research on the careers patterns and productivity of researchers has focused on scientists rather than engineers, specific job changes rather than the career as a whole, and publication productivity measures rather than patent outcomes. Moreover, much of the extant research on academic researchers has focused exclusively on the academic component of careers. As universities increasingly take on roles than were once considered the responsibility of the private sectorsuch as securing patentsand build greater ties with industry, it is timely to reexamine the nature of the contemporary academic career.
In this research, I draw on scientific and technical human capital theory to situate the central research question. Specifically, I examine the nature of the career pattern and publication and patent rates of scientists and engineers affiliated with federally-supported science and engineering research centers. The research makes use of curriculum vita (CV) data collected through the Research Value Mapping Program headquartered at the School of Public Policy. Tobit, Poisson, and Neural Network models are used in analyzing the data. In addition, I examine the career patterns of highly productive scholars and contrast those with less productive scholars.
The findings suggest that the ways in which academic productivity and career patterns have been conceived may be in need of revision, with a greater attention to diverse productivity outcomes and diverse career patterns. Some of the interpretations of empirical findings in the literature may be misconceived. Moreover, it may be the case that postdoctoral fellowshipa common component of government support for scientific and engineering researchmay be associated with lower career productivity rates.
This research contributes to our understanding of research careers with implications for public research policies. Finally, the relatively new method of analyzing CVs and appropriate modeling techniques and the challenges posed by this method are discussed.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5268 |
| Date | 03 March 2004 |
| Creators | Dietz, James Scott |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 534894 bytes, application/pdf |
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