A canon is a blunt force instrument: data science, canons, and generative frictions

Spatially close, though worlds apart. The contributors to this commentary - ‘we’; ‘us’ - conduct research and teach on data and technology-related issues at three Dutch universities. Some of us work at the same departments, and teach in the same programmes. We bump into one another during our daily commutes, and replenish our energy levels with the help of the same coffee machines after our lectures. We talk, and sometimes even discuss our research with one another. But do we also understand each other? What would that even mean? When we talk about ‘data’, do we talk about the same thing? Is that even necessary? What does ‘science’ for each of us entail? What does this mean for the education we collectively provide? What is the direction - scientifically, ethically, politically - the bachelor programmes we are all involved in head toward?
National science policy in the Netherlands, as well as at the level of universities themselves, tends to prioritise in various ways computer and computational sciences over the social sciences and humanities (Taylor et al., 2023). We feel that the oppositions that are produced and reinforced through such policies are both false and unproductive, and this collective uneasiness motivated some of us to initiate a conversation about what it would mean to think and work together. How do our academic lives ‘hang together’ (Mol, 2014) beyond our encounters near coffee machines in the hallways, and our names on the timetables the students would find when logging in to their university pages?

When asking these and many other questions, we realised that we lacked the language, a common vocabulary, to not only answer the questions with which we started, but also ask them.
Not only did many of key concepts used in our research and education - data, algorithm, ethics, ontology, law - mean and do different things for all of us, but concepts indispensable to some - e.g. justice -, would be nonexistent in the disciplinary universe of others.

We therefore needed to take a step back and reflect on how to have a conversation without sharing a common language. Our provisional solution was to take what we dubbed as ‘canonical objects’ as the focal points in our discussions. We borrow the notion of the canon from literary criticism, where it is used to mean a body of literature that over time comes to be taught as defining a particular culture (Bloom, 1994). For this reason, the canon has also been the focus of decolonial critics, who argue that we should critically interrogate the hegemonic discourses of Western culture (Spivak, 1990).

Based on this notion, we started to analyse concepts which each of us consider conceptually stable enough in our different disciplines that they might be taught on a bachelor’s-level course. We, in other words, took our disciplinary backgrounds and educational responsibilities as conversational starting points. Our roughly defined meta-question was how our disciplinary backgrounds produced different conceptions of the same terms, how these differences could be generative or problematic, and how our disciplines become invested in a particular interpretation?

What we called canonical objects is also strongly related to how some of us used and understood the notion of boundary objects. A classic definition of boundary objects is that these “have different meanings in different social worlds but their structure is common enough to more than one world to make them recognizable, a means of translation.” (Star & Griesemer, 1989). Boundary objects thus allow different ‘social worlds’ to work together without requiring them to be able to (completely) understand one another. If our canonical objects would indeed function like boundary objects, we would have to find out and explicate in what way we would be working together, and how these concepts help us do that.

As part of our exploration we also include answers from the generative large language model ChatGPT3.5. This LLM draws on internet content, and therefore offers a generalised and social version of the canon, replicating the most common tropes about our chosen objects of study available online.

Furthermore, interesting both conceptually and practically, was and still is, our attempt to create some level of mutual understanding (Gadamer, 2014), potentially with the help of boundary objects whose functioning depends on a lack of mutual understanding. How does our attempt to foster understanding about how we hang together or not, change our collaborations? What does this attempt do to the canonical objects that we used as conversational lubricants? How, to put that differently, does discussion and explicating our disciplinary divisions, change our capacities to e.g. teach together? And subsequently, what are generative but also less and non-generative ways of disagreeing with one another?

In this contribution we present the results of the conversation we have had so far about two canonical concepts: ‘AI’ and ‘trust’. Together we made a list of potential canonical concepts (see the Appendix) - so concepts that would be taught in a BSc/BA program/course - and from this list picked two of those with the most multifaceted disciplinary usage to discuss here. Each of us was asked to briefly explain how from their (disciplinary) point of view the concept was understood and taught in our undergraduate programmes. These brief reflections are accompanied by statements about our own positionality (Harding, 1989; Haraway, 1991) in which each of us situates him/herself in the academic tradition in which they were educated. We have included these because we presumed that academic disciplines (and what have been termed signature pedagogies, Poole, 2009) were and still are the key factors that influence the types of academic social worlds most of us live in. In the discussion we present some of the themes that emerged in our conversation, and that help to understand how our academic activities hang together - or not.