Production, Migration, & Differentiation: Analyzing the Co-Evolution of Careers & Knowledge Production

Doctoral recipients represent a vital role in the economy, by populating the science and engineering workforce, facilitating the transfer of technical and tacit knowledge among industry, government, and academia, and replenishing the intellectual stock of universities. And yet, our performance metrics reflecting university contributions to the economy largely overlook the production of scientists, which is arguably our most important source of technology transfer. While the conditions for innovation, commercialization, and publication of science are well-studied, the conditions for the cultivation of scientists remains black-boxed. This proposal pursues a line of inquiry that expands contemporary focus on the production of science to 1.) include the production of scientists; 2.) capture the co-evolution of knowledge production and careers; and 3.) build towards tractable and actionable metrics for universities and science. We first address the antecedents of the production of scientists in terms of the context where scientists train as well as the effects of this training in terms of where scientists go on to work and on the kinds of knowledge they produce. To do so we create a relational model of scientific production that advances our theoretical, empirical, and policy understanding of scientific knowledge production, technology transfer, and performance metrics in scientific innovation. We ask: 1. how do demographic, career, and resource factors explain not only the production of science but also the production of scientists; where are there, if any, points of convergence or contradiction in these predictors? 2. how do these demographic, career, and resource factors in the lab where students train shape post-graduate career and knowledge production choices; where are there, if any, features of how labs are organized that shape these career and knowledge production choices? 3. How might the lab where graduates train and where graduates go on to work shape the form and content of post-graduate knowledge production? We address these questions in the context of materials science and engineering (MSE) utilizing multi-level comparative research design at the individual, lab, and organization (department and university) levels. We combine qualitative interview and archival analysis with inferential statistics and computational linguistics. Intellectual Merit. The intellectual merit is multiplex. First the proposal is well-reasoned in that the research questions represent a logically ordered set of analyses attentive to and extending beyond the microfoundations of scientific careers and knowledge production, which is an established area of expertise of the PI and well-resourced feature of the team through the expertise and contacts of the diverse researchers and advisors. Furthermore, the project entails creative and novel combinations of archival, interview, statistical, and computational methods across analytic levels in service to research questions that have yet to be examined. The research is transformative because it finally incorporates and measures the highly touted, yet empirically overlooked, scientific output of doctoral student production—what many argue is our nation’s best form of technology transfer and critical to our knowledge economy—thereby making it of high intellectual and societal relevance. In doing so, the project finally elevates doctoral student production and impact to the level of our current performance metrics, scientific productivity measures, and stock of knowledge in the social sciences. Due to