On the Biomedical Elite: Inequality and Stasis in Scientific Knowledge Production

Abstract

Researchers and research institutes are increasingly being evaluated using metrics (from bibliometrics to patent counts), which are core instruments of a longstanding effort to quantify scientific productivity and worth. Here, we examine the relationship between commonly used metrics and funding levels for investigators funded by the National Institutes of Health, the largest public funder of biomedical research in the United States, in the years 1985-2015. We find that funding inequality has been rising since 1985, with a small segment of investigators and institutes getting an increasing proportion of funds, and that investigators who start in the top funding ranks tend to stay there (which results in stasis, or lack of mobility). Furthermore, funding levels are a strong quantitative predictor of the interrelated set of metrics frequently used by economists and policy makers to evaluate scientific research. Our results suggest that the widespread system of metrics favors a minority of elite, highly funded researchers and institutes. Current attempts to "optimize" science are inextricably linked to the concentration of funds in the biomedical research system and are likely to further reduce diversity in the research community.

The National Institutes of Health needs to better balance funding distributions among US institutions

“…1), is a clear example of the Matthew effect in action ("the rich get richer") (15,16). As one might expect under the Matthew effect, this funding inequality has been growing for at least three decades with a small segment of institutions getting an increasing proportion of funds (17). In other words, the impacts of annual differences in the likelihood of securing funding and of award sizes between institutions compound over time, which reinforces the monopolization of resources by favored institutions.…”

AUTOPILOT: Automating experiments with lots of Raspberry Pis

“…We think the expense of a system is important for two reasons: scientific equity and statistical power. The distribution of scientific funding is highly skewed, with a large proportion of research funding concentrated in relatively few labs [18]. Lower research costs benefit all scientists, but lower instrumentation costs directly increase the accessibility of state-of-the-art experiments to labs with less funding.…”

Global health enabling systems

“…Fujimura (1988) describes articulation work as those scientific activities necessary for acquiring resources and generating publications, such as project planning and decision making, reviewing existing literature, communicating with other scientists, and writing articles, the sort of work that does not directly generate data but builds careers. It is what the most successful US principal investigators grumble about keeping them from their lab benches, but also what gives them access to most of the funding available for research, more publications in top journals, and more patents (Katz and Matter 2017). Articulation work includes some, but far from all, of the knowledge that is produced in a project.…”