Wednesday@Hariri: Meet our Fellows James Feigenbaum
The recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. However, linking with simple algorithms is challenging when data is enumerated and transcribed with error and names are common and changing over time and hand linking, though accurate, is expensive, slow, and not replicable. I will present a machine learning approach that trains on the actual matches made by a skilled researcher or genealogist to make implicit linking rules explicit. Also, I will present preliminary results from two new projects exploiting linked data to demonstrate the possibilities of the complete count of historical censuses. First, I will use changes in name patterns among the African American population from 1860 to 1870 to predict antebellum enslavement status and trace forward the effects of enslavement intra- and intergenerationally. Second, I will use genealogically linked data to follow women across censuses and through name-changes at marriage to study the effects of automation and the technological destruction of a common occupation—local telephone operators.