In 2019 we received a grant to support “Digital Dostoevsky,” a large digital humanities (DH) project that aimed to create the first TEI edition of nineteenth-century writer Fyodor Dostoevsky’s major novels in their original Russian.1 We wrote the grant proposal ambitiously, as Dostoevsky scholars who were relatively new to the field of DH. We modeled our project on those we knew, and it heavily incorporated institutional infrastructures for training, consultation, and computing.2 In our original vision, we had included funds to attend the Digital Humanities Summer Institute to train on TEI, as well as to fly in consulting experts in the TEI to Toronto to train the team in person on aspects of the TEI specific to the project. Unfortunately, a few months after we received the grant, the global COVID-19 pandemic struck, closing in-person learning in Canada for most of the next two years. This necessitated a pivot in plans and a reevaluation of our working practices: “Digital Dostoevsky” changed from an institutionally managed and supported project to one rooted in a minimal computing ethos.3 This article will discuss the way that minimal computing methods transformed the project’s aims, particularly through the experience of minimalist DH pedagogy in the context of an undergraduate training workshop that we led in 2022.
Roopika Risam and Alex Gil (2022) have described minimalist DH computing as a “heuristic” comprising four key questions: “1) ‘what do we need?’; 2) ‘what do we have’; 3) ‘what must we prioritize?’; and 4) ‘what are we willing to give up?’” The reevaluation of our plans in the context of the pandemic forced us to strategically confront this heuristic. In our original budget, we had addressed these questions only superficially. Grant writing is often a practice rooted in a form of speculative fiction; we projected our ideal scenario onto our proposal and were rewarded for our hubris. Considering Risam and Gil’s four questions, we were unwilling to compromise on the methodological base of our project (the TEI), and therefore needed to find a new way of learning text encoding fundamentals; the training we had planned to undertake was now impossible and we needed to train ourselves. The initial context of the pandemic meant that we had to rely on our own computing environments, without additional technologies, infrastructures, or synchronous support as institutional resources that we had planned to use were fully engaged by the sudden shift to online learning midsemester. This experience led to a more collaborative and supportive team structure, stronger communication practices, and more robust investment in the project from all team members, demonstrating that a minimalist approach is the best method for our project.
The minimalist DH computing heuristic proposed by Risam and Gil helps to prioritize not just material and practical considerations, but also ethical and humanistic decision-making. By 2022, when we proposed an undergraduate research workshop for the Jackman Scholars-in-Residence (SiR) program at the University of Toronto (U of T), we leaned into minimal computing methods and practices, and the results were transformative for our students as well as the project as a whole. Minimalist DH pedagogy empowers student learning, giving students ownership of their knowledge and skills. In the context of “Digital Dostoevsky,” this approach had knock-on effects not just for our project, but also for our students’ professional development and life trajectories.
TEI as Minimal Computing
“Digital Dostoevsky” uses a corpus of texts by Dostoevsky.4 It relies on standardized guidelines by the Text Encoding Initiative (TEI) for encoding digital texts with Extensible Markup Language (XML) (TEI Consortium 2025). TEI allows for a variety of metadata to be embedded into the text; for example, structural elements like chapter headings and paragraph breaks as well as character identifiers, geographical locations, etc. Encoding a text using TEI-XML enables a computer to parse it and identify its encoded elements. The TEI Guidelines provide a standard that allows TEI editions to be understood broadly by those familiar with this widespread method, while also ensuring compatibility with other tools and systems.
TEI is an example of minimalist DH computing because it does not require many resources to implement and has a gentle learning curve. All that is needed is a plain text document, an XML editing program (freeware versions of these exist such as Visual Studio Code), and a copy of the TEI Guidelines (provided open access by the TEI Consortium). When we set out to teach ourselves TEI, after our training plans fell through in 2020, we relied on open educational resources, among them a startup resource, “Text and <pointyBrackets/>” (Hawkins 2020), and a self-guided TEI course, “TEI by Example” (Terras, Vanhoutte, and Van den Branden 2020).
In 2020, when the original project team, which comprised a small group working at U of T (PI Kate Holland and graduate Research Assistants Braxton Boyer and Lena Vasileva) and co-PI Katherine Bowers, located at the University of British Columbia (UBC), began to encode our first text, Dostoevsky’s novella The Double (1846), we immediately ran into the issue of how to work on the same text as a team. TEI encoding of a corpus raises three challenges for collaborative work: how to communicate and discuss encoding issues that arise, how to document encoding decisions to ensure consistency, and how to work simultaneously on and save XML files without creating conflicted versions. We were able to address all three with free versions of proprietary software. As a team, we discussed problems in the Slack communication platform or in regular Zoom meetings. Once a decision about how we would universally encode was made, this would be recorded in a shared Google Doc. Later, all encoding decisions were collected and organized in a shared Google Sheet. We chose to use the Google suite of apps because it was easy for the team, but first we discussed privacy concerns with Google’s use of data and overall security; because our data is not sensitive or personal, the team collectively agreed that Google provided a good solution.5 Finally, creating a free GitHub account for the project and depositing our encoded text there allowed us to safely work on the same document without fear of overwriting and also preserved our encoded files so that the whole team could easily access them.6 This cloud-based infrastructure allows for easy communication, documentation, and preservation.7
Designing a Minimal Computing TEI Training Workshop
The SiR program at U of T provides funding support and professional development activities to undergraduate students to work with a professor on their research. In 2022, it had a special call for online projects to accommodate the slow return from lockdown following the height of the pandemic. We successfully applied for a workshop called “Digital Dostoevsky: Reading Russian Novels with Computers,” allowing us to bring six undergraduate students onto the project in May 2022. Then we set out to design an undergraduate TEI training course to onboard the SiR students. In our design, we extended the minimalist DH computing model that the project already exemplified onto the pedagogical plane.
When we turned to Risam and Gil’s heuristic, we realized that the method (the TEI) must be central to the workshop’s design. We aimed to train the students in the TEI’s basic principles, assist them in learning how to use the necessary tools (an XML editor and GitHub), and provide them with a primer showing how our project specifically uses the TEI to encode Dostoevsky’s novels. We also introduced them to the same communication structures that we use. In prioritizing the method and its requirements, we were able to distill down our needs for the workshop to three main categories: training, the TEI, and communication.
At the same time, we acknowledged that we were gaining the opportunity to train six new team members, each with relevant expertise—five literature or media studies and one computer science student, all advanced or native Russian speakers. We were also gaining a month of dedicated time for training and encoding work. We would never have an opportunity like this again, but it required a compromise. In order for our workshop to succeed, we needed to trust our students and give them intellectual autonomy over the work they were going to do. Thus, we relinquished direct oversight over the individual encoding decisions each student was making.
Throughout the workshop, we emphasized three key working principles: (1) collaborative practice—we are in this together, (2) ask for help if you have questions, and (3) your work matters. Since the students were working on texts from our corpus that we had not yet encoded, we emphasized that their work would be important. The encoding of the corpus is a crucial first step in the research that we want to do, and each interpretive decision they make is a decision that informs our later analysis. We were creating the encoded corpus together, as a collaborative research project.
Training
The SiR workshop met for four hours each morning, Monday through Friday, for four weeks in May. In the afternoons, participating students had professional development programming provided by the Jackman organizers. The workshop was conducted entirely on Zoom. U of T provided educational Zoom accounts to all of its faculty, which enabled us to use this video conferencing platform without time constraints. In our first meeting, we began with an overview of what DH is and what “Digital Dostoevsky” is trying to achieve using DH methods. We described TEI as a method that enables other analysis and gave examples of possible outputs, such as a character network graph. Then we invited students to do two training tutorials: “Text and <pointyBrackets/>” and “TEI by Example.” We knew that these two tutorials would bring students up to the basic level of TEI-XML knowledge that we need for our project since we had trained on them ourselves. Our familiarity with the tutorials also meant that we could develop our own additional materials to supplement and tie the students’ learning of the TEI to best practices for “Digital Dostoevsky.”
“Text and <pointyBrackets/>,” a brief tutorial developed by historian Michael Hawkins (2020), introduces users to the fundamentals of XML encoding and GitHub. For writing XML, it recommends using Atom, GitHub’s then freeware XML editor (which has since been discontinued).8 It also shows users how to create and use a Git repository to store their XML transcriptions as version control best practice. This tutorial offers step-by-step instructions for installing Atom XML, setting up GitHub, and then coding a short XML document. Users work directly with these tools and the resulting “Hello World” XML document is published to the user’s GitHub repository. Each student worked on their own personal computer; the relatively small memory requirement of the software needed meant that all students were able to participate fully. Technical issues arose not because of inadequate computing hardware, but because of differences between Mac and PC software user interfaces. Two members of the original team worked on PC and two on Mac, so we were able to address these issues as they came up. Working through “Text and <pointyBrackets/>” collaboratively ensured that all the SiR students had an XML editor correctly installed on their own computer and that everyone had a working GitHub account. We also made sure that all students could access the “Digital Dostoevsky” repository and work with our corpus files at this point.
To introduce the TEI, we turned to “TEI by Example,” a resource for learning TEI basics developed by a consortium of European DH centers (Terras, Vanhoutte, and Van den Branden 2020). This resource consists of nine modules, each introducing one aspect of the TEI: introduction to text encoding, common structure and elements, the TEI header, prose, poetry, drama, primary sources, critical editing, and customizing TEI schemas. TEI schemas are documents that provide a lexicon of tags specific to a project. Each module includes an extended prose tutorial, resources for further reading, a what’s next section, and a bibliography, in addition to examples, tests, and exercises. “TEI by Example” also provides an on-site XML editor that allows users to test and validate their TEI-XML encoding for each module without worrying about an external editor, GitHub, or any other tools. Users work through a series of TEI-XML tasks that build on each other. We asked students to work through the extended prose tutorial, exercises, and tests of the first four modules: the introduction, common structure, the TEI header, and prose. This was all students needed to begin encoding the “Digital Dostoevsky” corpus.
Once students finished the first module, they began working through the next three modules at their own pace. We instituted individual breakout rooms, which provided a space for quiet focus on the task, but a workshop leader could jump in if the student had a question. Thus, students were supported but everyone was able to progress without interruption. Slack provided an easy means for communication across breakout rooms and the main Zoom room, which the Zoom software lacks. When students finished the tutorials, they each chose a text from our corpus to be “theirs” and we worked collaboratively on their TEI headers in the Atom XML editor, as we had worked together on the tutorials. By the end of the first week, all students had moved on to encoding the body of “their” text independently. The shift of the breakout rooms from each student’s individual working space to a working space dedicated to “their” text, with a corresponding Slack channel for queries related to it, gave them an additional sense of ownership over their work.
Automatic Tagging
When the “Digital Dostoevsky” team encoded The Double, we began with a plain text file and added individual TEI-XML tags to it to encode formal features, character names, place names, and speech. We had to decide what to encode, as well as how to encode it. The process of encoding and checking this, the shortest novella in our corpus, took the four original team members approximately three months. It became clear that asking the question “What do we prioritize?” was crucial for adapting our working methods for the SiR workshop. Our plain texts had already been cleaned by Boyer and Vasileva early on in the project (in 2019–20). In preparation for the workshop, we explored options for speeding up the encoding process to maximize the time with the students and narrow down the task at hand. We contracted a developer, Simon Wiles, to help with this problem.
In consultation with the team, Wiles created an automatic tagger that could, with some degree of accuracy, parse a Russian language plain text file and add basic, empty tags around some elements (formal structures such as paragraph breaks, character names, and place names) as well as a rudimentary TEI header (Wiles 2022). Wiles, who has a background in Asian languages, had no knowledge of Russian, but this did not present an issue as encoding Russian language with TEI is straightforward. For character names and place names, Wiles relied on named entity recognition. However, we also wanted Wiles’s tagger to identify and add <said></said> tags around speech. This turned out to be more complicated in Russian than it is in English as written Russian uses four different ways of indicating speech. Wiles worked closely with Vasileva, a native Russian-speaking graduate RA on the project, to refine ways of identifying speech in Dostoevsky’s texts. After significant consultation and revision, Wiles successfully created a tagger that can identify Russian speech with more than 80% accuracy.
Running Wiles’s tagger on our plain text files resulted in XML formatted TEI editions of our remaining six corpus texts. These gave the SiR students a head start on the encoding but still required significant work. All of the tags that Wiles’s tagger added were empty. So, for example, <said></said> tags were added around most of the speech but, in our corpus, we add attributes to speech, such as speaker, addressee, whether the speech is aloud and whether the speech is direct. The students were confronted with <said> tags that looked like this: <said who=“ ” toWhom=“ ” aloud=“ ” direct=“ ”></said>. Similarly, we tag each part of a name, but students were confronted only with empty <persName ref=“ ”></persName> tags and needed to add XML identifiers (XML IDs) and indicate whether this was a first name, surname, etc. The automatic tagger gave students new to these Dostoevsky texts and also TEI-XML a starting point. Thus, they could prioritize the task at hand without, at first, worrying about fully formed XML or even how to identify what to tag first.
Collaboration and Communication
Each student worked on their “own” text but had to follow the basic guidelines of the “Digital Dostoevsky” project’s schema to ensure consistency. Since text encoding is frequently interpretive, it isn’t always clear what needs to be encoded or how. Thus, coders might need information about how an issue was handled in another text, or which TEI tag might be best for a certain situation. For instance, if Russian poet Alexander Pushkin is mentioned in several corpus texts, we need to make sure that each reference has the same XML ID. An XML ID allows an entity to be identified by the computer however it is named in the text. In the “Digital Dostoevsky” corpus, lists of XML IDs are organized in each text’s backmatter. As students created this backmatter, coming across different entities in their texts, we realized that we needed a means of collecting XML IDs in use across the corpus. We created shared Google Docs that all the students could check and edit to collect and categorize these universal XML IDs. As students worked on their own text encoding from our corpus, we continued to meet in Zoom, making use of breakout rooms for easy consultation with team members and question sessions.
In addition to the Google Docs and the Zoom breakout rooms, we introduced Slack as a communication platform where students could discuss queries specific to their texts. The following exchanges are examples of the kinds of conversational discussions about encoding conventions that the Slack enabled:
Student 1: “what should the place type tag be for Rus'? it wasn't exactly a country so i'm not sure what to use”
Bowers: “I say use type=‘historical entity’”
Student 1: “btw, should i put rus' in the universal tag doc?”
Bowers: “Yes! Russia uses #ROS so there’s even an obvious XML ID for Rus’”
Student 2: “Which tag should we use for other novels mentioned in the book? Just \q\? 🙂”
Bowers: “yes, just <q>”
Student 2: “Perfect, thank you! 😉”
Student 3: “I have more of a general question but I can't really type it out without being confusing, could someone hop in zapiski for a minute”
Holland: “Lena is popping in to help.”
Slack’s chat-based platform also facilitated short exchanges that were quick-paced and encouraged emoji use. It enabled easy discussions about tagging queries and even a convenient way to request one-on-one consultation in a Zoom breakout room. These discussions allowed students to get a feel for when they could take their own initiative and where they needed to follow project conventions. With time, the students asked fewer questions in the breakout rooms and more on Slack as its chat communication style didn’t require an interruption of the encoding rhythm. It allowed them to post queries asynchronously, catching queries that may have come up earlier and been forgotten in the moment or that emerged from discussion of other students’ queries, both in the chat and as quick responses.
This communication structure created benefits for the whole team and fostered the students’ experience of the workshop as a space of community. We instituted this structure for a more practical reason—to standardize the students’ encoding as much as possible, given our choice to eschew oversight in favor of encoding progress and student autonomy. However, the workshop’s communication channels had a transformative effect on the group’s cohesion. Although the students worked on different texts, Google Docs, Zoom, and Slack created a true sense of collaboration. Slack, in particular, helped to instill in the group a strong sense of community. An unexpected additional benefit of this communication structure meant that, when a student had to fly to Europe on short notice to sort out a visa matter, they were still able to fully participate while traveling (which they chose to do). The asynchronous possibility of these structures ensured accessibility, an unanticipated but welcome benefit.
Short-Term Results
In May, students made rapid progress on the encoding work. One even finished encoding the novella Notes from Underground (1864) in its entirety. Students’ intellectual investment in the project also grew as they saw their own progress and contribution to the work. The Jackman organizers asked each team to give a final presentation to all SiR participants and the “Digital Dostoevsky” students were excited to show others the work they had done. In the final presentation each gave a brief discussion of a research question that they themselves devised from their own encoding. These included, for example, “Demonology in The Brothers Karamazov,” in which a student asked, “How can we organize the types and hierarchies of demons in that novel?” Another was “How to encode non-Russian names and why we might want to preserve the foreign language original.” A third asked, “How might we encode when characters interrupt each other?” The students’ coding conundrums and decisions led to genuine insight into discourse strategies in the Dostoevskian novel.
After the short presentation, the students still wanted to expand upon the ideas they had developed over the course of the workshop. For this, our team’s blog, Digital Dostoevsky, proved a good medium.9 The blog is hosted on WordPress and has been used by the team since the project’s inception to record and reflect on the encoding process and discoveries made along the way. Several of the students chose to write a longer form blog post about their work on the project and the ideas from their final presentations. For example, Eden Zorne (2022) wrote about the challenge of untangling speech from thought in a confessional narrative when encoding Notes from Underground. Veronika Sizova (2022) discussed the difficulties of characters who are themselves fictional inventing fictional characters, a double layer of fictionality, and how she addressed this while encoding The Adolescent (1875).
By the end of the May workshop, five of the six SiR students wanted to continue their work on “Digital Dostoevsky” and we hired them as Research Assistants. They continued on the project until graduation, or even, in some cases, after graduation.
Minimalist Computing Costs
Adding five new Research Assistants to the project meant that we were able to complete encoding and checking the full corpus by fall 2024; coincidentally and happily this lined up with our grant’s end date. Between 2020, when we began encoding, and 2022, when we facilitated the SiR workshop, progress on encoding was slow. Adding the students provided momentum as we built a community with accountability structures such as regular meetings around the encoding process. In 2023, we considered leading a second SiR workshop but, because our original SiR students were still actively at work on the project, we determined that it would be better to continue this work rather than seek out new students. The 2022 SiR workshop was a one-off, but an immeasurably valuable one for our work. By the end of the summer of 2024, the “Digital Dostoevsky” team had encoded all of the corpus files, and these were checked in the fall of 2024. The “Digital Dostoevsky” project grant meant that we could pay Research Assistants and also pay for some software and infrastructure to support the project. However, because TEI is inherently a minimalist computing method, these costs were minimal. The bulk of our grant went to student salaries, particularly between 2022 and 2024.
Following the workshop, we reassessed our tools for both the encoding process and project communication. We continued to use our project GitHub repository and have expanded it since. As we have completed encoding and checking our TEI editions, we have made them public, as a shared resource for others who want to do this kind of research. The team began encoding in the Atom XML editor but, after this software was discontinued by Microsoft, we switched to Oxygen XML Editor and paid for a user-based Academic License for each team member ($158/2 years). We continued to use Slack, Zoom, and Google Docs as our main communication tools, but upgraded our Slack account to Slack Pro ($8.75/month), which meant that our chats remained visible for more than 90 days. Later, when the project was winding down, we saved and organized the chats in our shared cloud-based Sync folder as a log of our coding decisions and downgraded the account back to a free one. As we were no longer meeting daily, we established a biweekly Zoom check-in to discuss queries. However, with such a large group of overextended undergraduates, it quickly became apparent no one would be able to attend all the meetings. Our solution was to record the Zoom meetings and save the transcripts for circulation and consultation afterwards.
Long-Term Results
The SiR program enabled us to train and hire Research Assistants for our project with a skillset (native or near-native Russian language skills and an interest in literary analysis) that was challenging to find on just U of T’s St. George campus, where the “Digital Dostoevsky” project was based. U of T’s three campuses have somewhat differing student demographics. St. George, the flagship, places the most emphasis on traditional academic disciplines; it is the only campus of the three, for example, that houses a Department of Slavic and East European Languages and Cultures and offers coursework in this discipline. The other campuses, Mississauga and Scarborough, place more emphasis on professional development and training and historically attract a student body from a more diverse socioeconomic background. This includes more students from newcomer and working-class households. The SiR program’s inclusion of student applicants from all three campuses enabled us to find Research Assistants with advanced Russian, while also helping students from Mississauga and Scarborough seeking advanced Russian literature study to connect with our project.
Of the six undergraduate SiR students, five came from a literature or media studies background and one from computer science. Before the workshop, we expected that the student with the computer science background would have the easiest adjustment to the TEI but this did not turn out to be the case. It was easier for the literature and media studies students to learn TEI-XML and its conventions than for the computer science student to develop their literary analytical skills, although admittedly, the SiR workshop we designed assumed some background in literary studies. The computer science student found it challenging to identify individual voices within the Dostoevsky text. Dostoevsky’s use of voice is complex but learning to identify voice is an integral component of the undergraduate literary studies curriculum. All the students sometimes had questions about voice in the text but easily fell into a kind of literature classroom mode, where group discussion of these issues is familiar. However, the computer science student could not easily articulate their questions in this regard. We provided one-on-one consultation for issues of literary interpretation that arose for this student, as we provided encoding support for the others. If we were to design another TEI training workshop for students from a similar mixed background, a module introducing literary analysis would need to be included.
For the literature and media studies students, the interpretive emphasis of TEI opened up a broader perspective on textual analysis. Two of these students had already been taking Russian literature classes at U of T’s St. George campus but the other three had only studied anglophone literature at the other two campuses before joining the workshop. That the two students already taking Russian literature, both of whom had studied Russian language from beginner to advanced levels at U of T, decided to embark on postgraduate study in that area was not surprising. However, another of the students ended up completing an MA in Russian literature and going on to a PhD program as a direct result of their participation in the SiR program, which introduced them to the discipline of Russian literary studies. Another applied for a master’s degree in library science/data management. This student had not considered this field before the SiR workshop but became invested in metadata and data organization over the course of their work on “Digital Dostoevsky.” In both cases, these outcomes were directly linked to mentorship of the students by Holland, Bowers, Boyer, and Vasileva.
For us, the SiR workshop had a long term pay-off beyond the completion of the coding and the professional accomplishments of the students. As Russian literature professors working in North America, we almost always teach using English language translations. Teaching close reading mediated through translation creates distance between the analysis and the text.[10] As Dostoevsky scholars, our research focuses on the minute linguistic details and discursive layers of Dostoevsky’s original Russian usage. Working closely for a month with students reading Dostoevsky in Russian and focusing on the nuances of meaning allowed us collectively to experience a closer engagement with the text than we are ever able to achieve in our respective classrooms. Insights from the rich Zoom discussions from the SiR workshop have contributed to our research and teaching. Furthermore, the “Digital Dostoevsky” Slack channels and Google Docs are a significant resource documenting three years of collaborative discussions and resolutions of textual and semantic ambiguity. The SiR workshop proved an incredible way to build a team. Through it, we created a literary lab for Dostoevsky studies.
Conclusion
Our pivot from thinking about our project as a large, institutionally-reliant one to a minimalist DH computing one that we have more control over had a transformative result on our working practices. This change can be most clearly seen in the way we approached planning the 2022 SiR workshop “Digital Dostoevsky: Reading Russian Novels with Computers” and our students’ outcomes. Having prioritized the method, training, and communication, we sacrificed direct oversight over the students. Although originally, we had thought of this as a potential problem, it ended up becoming a virtue because it gave the students more autonomy, ownership over their work, and long-term intellectual investment in the project. Thus, the accounting process that a minimalist DH computing approach entailed ended up benefitting our project significantly.
What does it mean to teach DH with minimal infrastructure? For us, it means we do our own training, using materials that are available online or that we have created. It means anyone can do the work with whatever computer they already have. It means the learning curve from no knowledge to independent work is not a steep one. It means that students who are onboarded using this method can play an integral and meaningful part in the process of decision-based TEI encoding. Our project would not exist in the same way it does without the SiR students. They bring different perspectives and questions from us, and from each other. Teaching students to encode has resulted in a richer corpus.
Minimalist DH enabled the creation of interconnected but separate spaces in which students are able to work as a team, collaborating and checking in, but also independently making interpretive decisions and building up their confidence with the method and the material text. These digital spaces include Zoom rooms and breakout rooms, Slack channels and chats, shared Google Docs and GitHub repositories. They became, in fact, even more creative than the traditional literary classroom: a laboratory where students and professors discussed and debated the nuances of Dostoevsky’s stylistic quirks and tics in the original Russian texts. Thus, they also morphed into collaborative intellectual spaces of textual analysis and engagement.
The minimal computing environment facilitated by the SiR workshop created a real sense of a team, a collective feeling of scholarly impetus, among both students and the “Digital Dostoevsky” project members. Without the SiR students’ active and enthusiastic participation in the project, the “Digital Dostoevsky” corpus, which is now fully encoded, would not have gotten to this state as quickly as it did.[11] The 2022 “Digital Dostoevsky” SiR workshop is an example of minimalist DH pedagogy that has shown the way minimal computing methods can empower students in their learning and professional development as well as strengthen a DH project, creating spaces for inclusivity and experimentation that have far-reaching impact.
