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Political scientists are wary of engaging with 'the public' on mainstream and social media because they fear those mediums fail to get across the deep and nuanced argument they develop in their own research. This article suggests a way of justifying public engagement that begins not with debates about the ethical and political concerns of doing this in practice (of which there are many), but how we as political scientists justify public media engagement to ourselves on the basis of the ethical and political process of 'doing' political science. As such, this article identifies the disciplinary basis upon which we may justify media-driven public engagement as an integral part of political science as an academic enterprise. Drawing on current epistemological debates in political science, the article characterises moments of political research as impressionistic exercises, which require public engagement. This means making the public aware of the deep and valuable insights of political science, in a way that sketches out how the discipline can shed light on important social and political phenomena, thereby informing our own scholarly thinking, and that of those we engage with.

A review essay covering books by 1) Malcolm M. Feeley and Edward Rubin, Federalism: Political Identity and Tragic Compromise (2008), 2) Lauren Benton, A Search for Sovereignty: Law and Geography in European Empires 1400-1900, 2010) and 3) Norman Davies, Vanished Kingdoms: The History of Half-forgotten Europe (2011).

Psychology departments seldom take their students back to the thicket of Freud's Collected Works. Medical schools turn to Hippocrates mostly for his oath, not for his skills at analyzing the pathologies of female hysteria. Those learning to study the universe today do not work through the elliptical paths of the stars and planets that Ptolemy developed so that he might keep the earth at the center of things. So, why should the discipline of political science be any different? Why should we teach our students about those methods employed by political scientists generations ago, methods that often look quite primitive next to the sophisticated tools of analysis and measurement that dominate current political science curricula?Why should one resist the forces that drive a discipline and a society to a sort of methodological amnesia? History may repeat itself, as the adage suggests, but methods of analysis often build on themselves to correct past inadequacies or they are replaced by those more able to address with precision the concerns of the discipline.

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.