NSF Award
2122232
Despite efforts at diversification, an outsized proportion of prestigious NIH R01 awards go to a circumscribed group of individuals and institutions. How and why does this happen? The Matthew Effect, whereby success begets success, is thought to be responsible: applicants with even small advantage at the outset may have their advantage multiplied many times over following initial success. Evidence is consistent with the presence of a Matthew Effect in R01 funding, yet no study has illuminated the specific nature of the advantage, nor detailed the means by which advantage is multiplied and accumulated. This project will answer questions about which aspects of social capital and scholarly achievement contribute most to R01 success, and whether gender or timing of scholarly events contribute, by examining the individual career trajectories of awardees of NIH Mentored Career Development Awards (MK awards). Project results will help design effective interventions to avert unintended funding disparities, while maintaining a rigorous peer review system. <br/><br/>This will be the first empirical test of a Matthew Mechanism during transition to research independence and the first to leverage heterogeneous scholarly graphs (HSGs). The first aim is to capture complex relationships between each MK awardee, their scholarly achievement and social capital, and R01 success during their quest for research independence. Existing bibliographic and NIH award data will be combined in the construction of a “global” HSG database - relating all MK awardees to their associated scholarly objects. The result will be a comprehensive graph structured database in which nodes represent all MK awardees and their associated scholarly objects (e.g., published articles, journals, primary academic institution, coauthors, coauthor’s scholarly objects), and edges represent relationships of various types (e.g., author of, cited by, affiliation, research topics). Relationship context will be captured for all scholarly objects in the HSG through global, local, and hyper-local graphical feature extraction to comprehensively characterize MK awardees’ scholarly profiles. Second, survival models will be developed to predict R01 success for MK awardees from latent and observed variables of scholarly achievement and social capital, and global, local, and hyper-local HSG features. The study offers a novel approach to studying complex social processes that marries social capital theory with heterogeneous scholarly graphs and network science methods. This study will go beyond previous studies in providing a multidimensional characterization of scholarly social capital (beyond coauthorship and citation) and will examine differential social capital accumulation as a mediator in MK to R01 transition. Empirically-grounded predictive models will be designed to probe existing theory and yield insights on social capital’s role in R01 funding success. This study will yield actionable knowledge to inform strategies aimed at improving efficiency awards and increasing the diversity of the awardee pool.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
