The diagnosis of ADHD in U.S. children roughly doubled in the last thirty years. The diagnosis of autism saw a five-fold increase over two decades, leading to widespread claims of an “autism epidemic.” In another realm, the SDGs (Sustainable Development Goals) have become a default shorthand for the global governance of development, introducing an infrastructure of dashboards, targets and indicators that are meant to tell us what success and failure look like. In yet another domain, the United States was ranked as the “most prepared” country to handle a pandemic shortly before COVID-19. We clearly live in a world where numbers, categories, and rankings shape how we understand ourselves and much around us. But how are these metrics actually determined? This course will examine what goes into forming categories and conducting measurement. Rather than taking numbers at face value, we will examine how systems of measurement are built: who defines categories, what assumptions are embedded in indicators, and how complex social, political and biological realities get translated into neat numbers. While numbers are typically framed as neutral and universal, the course will seek to show that measurement is seldom an innocent matter. We will examine case studies on a variety of different topics including disease and therapeutic categories, especially in mental health; how poverty gets measured; global governance through metrics; rankings of pandemic preparedness; alternative metrics of well being within indigenous knowledge systems; and more. In each instance, we will examine how seemingly objective metrics and data reflect power structures, values, and orientations of institutions. We will also consider how extant knowledge structures can be challenged, and how alternatives can be introduced.