Wayne de Fremery’s current book project, Computational Bibliography and the Sociology of Data, reinvigorates analytical bibliography by expanding the scope of what bibliography describes and by diversifying the forms used in bibliographic description. As etymologies of the word bibliography suggest, bibliographers have used bibliographic forms — books — to document books. Analytical bibliographers have typically investigated the materials and technologies used to create and circulate texts. Computational Bibliography and the Sociology of Data suggests expanding the scope of analytical bibliography to include the computational systems currently creating and circulating data. It suggests using computational methods to document computational systems in order to illuminate the materials and technologies expressing data, as well as to describe the socio-historical constraints within which people have worked to make and share data.

This talk will outline the broad arguments presented by Computational Bibliography and the Sociology of Data and then narrow its focus to reveal the deep relationship between traditional forms of bibliographic description and newer forms of artificial intelligence, especially those related to machine learning. The talk proposes inductive approaches of bibliographers such as W.W. Greg and those creating machine learning frameworks are homologous. It also suggests that the critiques of bibliography as an inductive science leveled by scholars such as D.F. McKenzie and Jerome McGann are isomorphic with critiques of current machine learning methods.