Who lets math organize their life?
“…technical practices in mathematics and philosophy in turn offered important tools for cultivating truer forms of spiritual and mental nobility. These practices enabled mathematics and natural philosophy to transform, discipline, and train the intellect, the senses, and the affects, and they put these trained faculties at the heart of organizing one’s life.”
(PG9-Good Life Scientific Revolution)
On October 9th, 2019, Dr. Matthew Jones, visited the Mellon Sawyer Seminar group to discuss how his work in the history of science and technology relates to, makes use of, and critically examines “data” and its artefacts. Themes of collaboration and ethics are two threads that run throughout Dr. Jones’ work, though these terms take on radically different meanings as a result of shifting socio-temporal contexts. His work covers an expansive time period, ranging from early modern inventions to contemporary concerns about digital privacy and surveillance.
The social nature of knowledge production and innovation were woven throughout our conversations with Dr. Jones. Highlighting collaborations between artisans and inventors in the mechanization of calculation (rather than narratives about exceptional individuals) is representative of a broader shift in historical study. Scholars from many fields have been moving away from the figure of the individual genius towards recognizing a more complicated and collaborative model of innovation. Similar reconceptions are happening within the contemporary discipline of digital humanities, as scholars strive to repatriate the credit for early experiments with computing to the workers, often women, who actually operated the machines on behalf of mostly male researchers (see Terras and Nyhan, 2016). As the saying goes, “no man is an island.”
One striking early modern example of collaboration is found in Jones’ tracing histories of inventions, particularly in moments of translation from paper design to material machines (see Reckoning with Matter). The connection between hand and mind is embodied as artisan and inventor in his exploration of the processes by which calculation became mechanized. It is remarkable that, though calculation was considered the materialization of thought, the automatization of that process resulted in a split between calculation and attribution of intelligence. Such a divorce not only “automates the boring stuff” but also (or perhaps because of this) shifts the valuation of human mental processes.
The relationship between designers and makers was necessary for the mechanization of calculation — each were highly proficient practitioners of a particular field of knowledge. In the past, present, and the future, such embedded specialists are one key to successful collaborative projects (for more keys, see “A Role Based Model for Successful Collaboration in Digital Art History” by Langmead et al.). The future of methods and tools then, might look like earlier instantiations of overlapping specialties (such as numismatics or genealogies). Like visual thinking, making processes of cognition material ultimately affects their form.
Knowledge production is a process of propositions. The iterative nature of scientific research is not unlike current scholarly practice, particularly in the realm of digital humanities. Research and intellectual output of the more “traditional” kind (books, articles, lectures), is often hidden from those not directly engaged in the processes themselves. Even graduate students in the humanities are often at a loss as to how books (apparently) emerge suddenly. This is evident in the multitude of books on how to write a dissertation or article, which often focus on the practical and/or hidden aspects of academic writing.
The digital humanities, as a developing discipline, attempts to be more transparent in its processes. Iterations have far more currency and use in digital projects, which reference where they are in the project lifecycle. Perhaps this is because the work that goes into producing “findings” is a more divisible process than the apparent “lightning” that strikes good ideas into the mind. Viewing a project as dynamic not only allows for seeing an idea in different moments of its development, but this practice also highlights how work is never (or should never) be “the answer.”
The historical context of Dr. Jones’ work — making explicit that the knowledge that we take for granted was at one time controversial and threatening — provides some comfort by reminding us that we have always struggled with accepting new technologies. It is also a reminder that once we do accept them, they can often become quickly invisible and taken for objective truth. Jones emphasizes that the work facing all of us, as educators and users of technology, is to reimagine the ways that data works in our lives while retaining the thread of knowledge that has historically shaped the ways that we learn to see the world today.
His approach to pushing back at the presumptions of truth in our technologies is simple: teach others to think critically about data, and its systems of collection, circulation, and manipulation. As he said in the forthcoming episode of the Information Ecosystems podcast, what is needed to do this work is not just humanists, or just technologists — and even beyond collaborations between them — is thinkers and academics with a foot on each side of the “fence,” and the wisdom to acknowledge that the fence is a construct, anyways. More practically put, we need scholars who know Python AND the history and theory of their field.
In his work with undergraduates, Jones reports that his students come into his class already skeptical of the idea of ‘cyberutopianism,’ an ideology perhaps made most famous by John Perry Barlow’s 1996 manifesto “A Declaration of the Independence of Cyberspace.” They come to class having grown up online, and with a strong understanding of the politics of the internet. They come, Jones says, searching for the tools and vocabulary to make what they already know to be true demonstrable in some meaningful way. This begins with a meta-understanding of data, through examining data documentation, and asking critical questions about the choices others have made before them regarding that data — who collected it, what data is missing, why these categories?
What comes from these explorations is not only practical coding and data skills, but also the understanding that knowledge means giving something up, whether it be an old way of doing things, access to resources, or the ‘purity’ of a dataset. But, as Jones reminded us during the Sawyer Seminar, being critical of technology and data isn’t the same thing as being a Luddite. By teaching and collaborating as open-minded skeptics we can better understand our predecessors as well as meet the challenges of the future.
Matthew Jones is the James R Barker Professor of Contemporary Civilization at Columbia University in the City of New York.
Sarah Reiff Conell is a PhD candidate in the History of Art and Architecture Department at the University of Pittsburgh. Her research traces the flow of miraculous agency through objects that participate in various forms of replication both within and across media.
Shack Hackney is a PhD candidate in Library and Information Science at the University of Pittsburgh’s School of Computing and Information. Their research focuses on structural inequality within digital infrastructure systems, particularly within the realm of digital character-encoding standards, and the ways that knowledge organization systems create physical and virtual spaces that privilege certain bodies and experiences over others.