Historian Paul Edwards has described climate science as A Vast Machine. Climate models incorporate knowledge from a wide range of disciplines including atmospheric physics, chemistry, ecology, and economics. Additionally, the rely on measurements collected by a wide range of instruments by a wide range of people with disparate priorities. These models are vital for organizations such as the Intergovernmental Panel on Climate Change (IPCC) to make predictions about the future climate and to form policy recommendations.
One task of social epistemology–what Alvin Goldman describes as “systems-oriented social epistemology” is to evaluate and make recommendations about the organizational structure of science. However, existing attempts to model science are too abstract to allow for meaningful normative conclusions. With my research I hope to offer an analysis of climate science that allows for such prescriptions.
This project uses citation analysis to understand how climate scientists interact and use each others’ work, particularly between disciplines. Citation information, along with other metadata, is gathered from a variety of sources (online repositories, Crossref’s database, the American Meteorological Society, the IPCC, and others), collected in a database, and analyzed using tools including Python, R, and Gephi.
This project began in earnest in February 2017. I am currently in the early stages of analyzing data and refining my procedures of collection and analysis. Below are a few images demonstrating some early experiments: