John Milton's Freedom of the Press Pamphlet Printers Found
An interdisciplinary team of CMU researchers use machine learning and statistical tools to determine origins of a 375-year-old document
By Stefanie Johndrow
Though John Milton’s "Areopagitica" — one of the most significant documents in the history of the freedom of the press — was first published 375 years ago, the printer of the pamphlet has — until now — remained unknown.
An interdisciplinary team of literary scholars, statisticians and computer scientists from Carnegie Mellon University has attributed the Nov. 23, 1644, printing of "Areopagitica" to the London printers Matthew Simmons and Thomas Paine, with the possible involvement of Gregory Dexter. The results of the research will be available in the Spring 2020 issue of Milton Studies.
"It's tremendous to celebrate Areopagitica's 375th birthday by learning something new about such a foundational document," said Christopher Warren, associate professor of English in CMU's Dietrich College of Humanities and Social Sciences and senior author on the paper.
For fear of persecution and punishment, printers in Britain from 1473 to 1800 declined to attach their names to about a quarter of known books and pamphlets, leaving the origin of many historical texts unidentified.
"In Milton's time, printers could be jailed and even executed for printing controversial material. While Milton’s printers joined him in rejecting the notion that ideas had to be licensed before they could be printed, they also needed the protection of anonymity," Warren said. "The reason we haven't known who printed 'Areopagitica' is directly tied to the reason Milton had to write it. Those of us who benefit from press freedoms and freedom of speech sometimes forget the risks early printers took in producing controversial materials."
Many of today's principles relating to freedom of speech and expression are based on "Areopagitica."
Pictured above: Impressions of damaged type can help identify a book's printers. A CMU team was able to compare type impressions more efficiently than prior methods have allowed through machine learning and statistical analysis.