I’m looking for engagement, critical reflection, and experimentation: Push yourself!

Active engagement

Don’t just take it in, be active in the class discussion. Leadership and positive contributions to the class discussion, wherever that takes place. Each session will involve hacking, poking, prodding, and otherwise doing digital humanities. We can spend time every session considering how all of this will play to the experiment as well.

Privacy

You are in no way obligated to do any of the public-facing work of this course under your own name. Pseudonyms are ok. You do not need to explain why you want to use a pseudonym to me. At all times, keep your own personal safety online front and centre: my experience of the internet, and of academic culture, will have been different from yours, and my goal is to listen more than I talk when it comes to these issues.

Assessment Breakdown

  • 40% Collegiality and Generous Thinking
  • 30% Method/Perspective Exploration
  • 30% DH Experiment

Collegiality and Generous Thinking

Generous Thinking [begins] by proposing that rooting the humanities in generosity, and in particular in the practices of thinking with rather than reflexively against both the people and the materials with which we work, might invite more productive relationships and conversations not just among scholars but between scholars and the surrounding community. - Kathleen Fitzpatrick

To demonstrate generous thinking and collegiality, I want you to be attentive to your peers’ annotations and notebooks, and engage with them thoroughly, whether by responding to an annotation, drawing the connections with other bodies of thought or artefacts, or by annotating someone else’s notebook. Ancillary to this, I also want to see you searching for opportunities to wonder aloud about how your discipline and dh might engage in the kind of generous thinking that Fitzpatrick describes.

Collegiality also entails attending and being prepared for all of our meetings.

This aspect of the course is graded exceptional / pass / fail.

Method/Perspective Exploration

You will select a particular method or perspective you want to explore in more depth. It might be that one of our visitors has also discussed aspects of the method or perspective, so if appropriate you’d be expected to engage with their work as well. Methods or Perspectives include but are not limited to:

     
archives & databases large language models as ‘ai’ scholarly publishing
accessibility & design sonification digital pedagogy
public humanities data feminism visualizations
minimal computing algorithmic writing: games, simulations computational creativity
text analysis webmapping & GIS network analyses
linked open data, knowledge graphs    

Annotate your top 3 topics that you’d be interested in exploring

DH Experiment

Using what you are learning, your will carry out an experiment using an approach or tool that is new to you, on a body of materials that are very familiar to you. Perhaps you have a paper from another class that you are fond or proud of; your experiment will be to re-cast those materials via an appropriate DH tool/technique/approach.

What has changed? What new things have you spotted? What ideas/observations have been confirmed? What does DH bring to the questions or issues? How do you think differently as a result? See Matthew Lincoln on ‘confabulation’ in the humanities.

An experiment poses a question, it describes and then uses a method, and it keeps track of what you think the likely outcome will be before you do the experiment, and what you observe afterwards.

You will submit to me:

  • a lab notebook with at least 10 entries; this notebook is a gift to future you when you try to replicate or revisit what you’ve learned
    • this will be a private repository on Github to which you’ll add shawngraham as a collaborator.
    • each entry will be a markdown file with a name along these lines ‘yyyy-mm-dd-entry#.md’
    • bullet points etc are fine; these are not essays. They are your notes. They can contain images, links, screenshots, to do lists, things that are working, workflows, reference literature etc
  • an unessay discussing the experiment, in a format appropriate to the work (thus, not necessarily a written essay)

Generative AI policy

‘AI’ is mostly a marketing term. But there may be cases where it is appropriate to use things like LLM (large language models) or other such tools in the context of exploring digital humanities’ work. For instance, it might make sense to use it

  • for understanding what code does
  • for making alterations to example code for a particular use
  • for expanding code to do new things.
  • when a tutorial or how-to employs such a technology and you wish to explore/experiment

However: you may NOT use such tools for ‘writing’ your lab notebook, because… what would be the point? The lab notebook is for Future You. So… just don’t, ok? It might make sense to use something like eg makereal to help develop an interface for an unessay. If you’re ever in doubt, just talk to me.

The key question I think might actually be: does using X diminish or enhance my humanity?

On which note, see what artist and researcher Eryk Salvaggio wrote recently:

Generative AI is digital humanities in reverse. What we describe about the information in an archive becomes a prescription – what was meant to be a set of notes becomes a recipe. This reversal makes digital humanities a key lens for understanding the complex cultural questions surrounding AI, but to do so, we have to get into the habit of thinking in reverse. We have to consider the consequences of the ways we organize information deliberately, & become literate about what AI systems will do to the data we gather about culture. Assume anything we record will eventually be reversed and mass-produced. How does that change the way you label, categorize, store information? What do you do differently? The opposite of digital humanities – issues around the preservation of scarce originals are displaced by the mass production of simulated derivatives.