18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Modeling research universities: Predicting probable futures of public vs. private and large vs. small research universities

      , ,
      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The future of the American academic research enterprise is considered. Data are presented that characterize the resources available for the 160 best-resourced research universities, a small subset of the 2,285 4-year, nonprofit, higher education institutions. A computational model of research universities was extended and used to simulate three strategic scenarios: status quo, steady decline in foreign graduate student enrollments, and downward tuition pressures from high-quality, online professional master’s programs. Four specific universities are modeled: large public and private, and small public and private. The former are at the top of the 160 in terms of resources, while the latter are at the bottom of the 160. The model’s projections suggest how universities might address these competitive forces. In some situations, it would be in the economic interests of these universities to restrict research activities to avoid the inherent subsidies these activities require. The computational projections portend the need for fundamental change of approaches to business for universities without large institutional resources.

          Related collections

          Most cited references4

          • Record: found
          • Abstract: found
          • Article: not found

          Quantifying the evolution of individual scientific impact.

          Despite the frequent use of numerous quantitative indicators to gauge the professional impact of a scientist, little is known about how scientific impact emerges and evolves in time. Here, we quantify the changes in impact and productivity throughout a career in science, finding that impact, as measured by influential publications, is distributed randomly within a scientist's sequence of publications. This random-impact rule allows us to formulate a stochastic model that uncouples the effects of productivity, individual ability, and luck and unveils the existence of universal patterns governing the emergence of scientific success. The model assigns a unique individual parameter Q to each scientist, which is stable during a career, and it accurately predicts the evolution of a scientist's impact, from the h-index to cumulative citations, and independent recognitions, such as prizes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Changing demographics of scientific careers: The rise of the temporary workforce

            Contemporary science has been characterized by an exponential growth in publications and a rise of team science. At the same time, there has been an increase in the number of awarded PhD degrees, which has not been accompanied by a similar expansion in the number of academic positions. In such a competitive environment, an important measure of academic success is the ability to maintain a long active career in science. In this paper, we study workforce trends in three scientific disciplines over half a century. We find dramatic shortening of careers of scientists across all three disciplines. The time over which half of the cohort has left the field has shortened from 35 y in the 1960s to only 5 y in the 2010s. In addition, we find a rapid rise (from 25 to 60% since the 1960s) of a group of scientists who spend their entire career only as supporting authors without having led a publication. Altogether, the fraction of entering researchers who achieve full careers has diminished, while the class of temporary scientists has escalated. We provide an interpretation of our empirical results in terms of a survival model from which we infer potential factors of success in scientific career survivability. Cohort attrition can be successfully modeled by a relatively simple hazard probability function. Although we find statistically significant trends between survivability and an author’s early productivity, neither productivity nor the citation impact of early work or the level of initial collaboration can serve as a reliable predictor of ultimate survivability.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Twin-Win Model: A human-centered approach to research success

              A 70-year-old simmering debate has erupted into vigorous battles over the most effective ways to conduct research. Well-established beliefs are being forcefully challenged by advocates of new research models. While there can be no final resolution to this battle, this paper offers the Twin-Win Model to guide teams of researchers, academic leaders, business managers, and government funding policymakers. The Twin-Win Model favors a problem-oriented approach to research, which encourages formation of teams to pursue the dual goals of breakthrough theories in published papers and validated solutions that are ready for widespread dissemination. The raised expectations of simultaneously pursuing foundational discoveries and powerful innovations are a step beyond traditional approaches that advocate basic research first. Evidence from citation analysis and researcher interviews suggests that simultaneous pursuit of both goals raises the chance of twin-win success.
                Bookmark

                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                December 11 2018
                December 11 2018
                December 11 2018
                December 11 2018
                : 115
                : 50
                : 12582-12589
                Article
                10.1073/pnas.1807174115
                6294910
                30530668
                8a84d4a2-19a6-4784-9237-d6d5a21d1adf
                © 2018
                History

                Comments

                Comment on this article