Further reading
For additional background on quantification in history, check the texts below:
Graham, Shawn, Milligan, Ian, Weingart, Scott and Martin, Kim (2022), The joys of big data for historians, in Exploring Big Historical Data. The Historians Macroscope (World Scientific; 2nd Edition), pp. 1-34.
Fourie, Johan (2023), Quantitative history and uncharted people, in Quantitative history and uncharted people. Case studies from the South African Past (Bloomsbury), pp. 1-32.
Lemercier, Claire and Zalc, Claire (2021), Back to the sources. Practicing and teaching quantitative history in the 2020s, Capitalism. A Journal of History and Economics, vol. 2, no. 2, pp. 473-508.
Blaxill, Luke (2023), Why do historians ignore digital analysis. Bring on the Luddites, The Political Quarterly, vol. 94, no. 2, pp. 279-289.
Jockers, Matthew L. (2013), Macroanalysis. Foundation, in Macroanalysis. Digital Methods and Literary History (University of Illinois Press), pp. 3-32.
For discussions on the use of computational methods in the humanities in general, see:
Mullen, Lincoln: Behind, ahead (March 9, 2026).
Posner, Miriam (2015), Humanities Data: A Necessary Contradiction, Blogpost.
Kirschenbaum, Matthew (2014), What is “Digital Humanities,” and why are they saying such terrible things about it?, Differences 25 (1). https://mkirschenbaum.wordpress.com/wp-content/uploads/2011/03/ade-final.pdf
Kirschenbaum, Matthew (2010), What is Digital Humanities and what’s it doing in English departments?, ADE Bulletin 50.
Fish, Stanley (2012), Mind Your P’s and B’s: The Digital Humanities and Interpretation, The New York Times (Jan 23, 2012).
Marche, Stephen (2012), Literature is not data, Los Angeles Review of Books (Oct 28, 2012).
Blevins, Cameron (2026), Bottlenecks, side quests, and the calculus of historical research (March 27, 2026).
Cohen, Dan (2025), The Writing Is on the Wall for Handwriting Recognition (Nov 25, 2025)
Blevins, Cameron (2025), Generative AI and History in 2025 (Dec 19, 2025)