Editors' Choice

Editors’ Choice: Making Marx More Readable: A Minimal Computing Approach to a Community-Driven Edition of Capital Vol. 1

Editors’ Summary: In “Making Marx More Readable: A Minimal Computing Approach to a Community-Driven Edition of Capital Vol. 1,” published in Digital Humanities Quarterly, Steven Gotzler and Avery Wiscomb present MARXdown, a minimal digital reading edition of Marx’s Capital Vol. 1, as a retrospective case study in applying minimal computing principles to community-driven DH projects. […]

Editors' Choice

DHNow Newsletter, July 22, 2026

This issue was curated by Colleen Nugent McLean, DHNow’s Editor, and Nico Larrondo, DHNow Guest Editor. Our Editors’ Choice selections this week include a discussion of the difficulties in digitizing at-risk media, a critique of “vintage” AI models such as Talkie, and an article introducing a minimal computing approach to teaching Marx’s Capital. We have […]

Editors' Choice

Editors’ Choice: Mozilla AI at Internet Archive Europe: Owning Your AI Stack

Editors’ Summary: This post reviews a conversation between researchers from Mozilla AI and Internet Archive Europe on computing infrastructure dependency. For digital humanists, we use cloud-based systems on a daily basis, whether that is to store research data, digitized primary sources, machine learning models, etc. As seen in events like AWS’s major outage in the […]

Editors' Choice

Editors’ Choice: Pre-revolution network connections of the 1989 Polish Round Table participants

Editors’ Summary: This paper, focusing on the Round Table meetings in Poland during the late 1980s, meetings that hoped to address political challenges in Communist Poland. Using network analysis, the authors present the considerations behind creating network data visualizations. The metadata that the authors consider, such as affiliations, personal biography, and others, are especially helpful […]

Editors' Choice

Editors’ Choice: Dataset Context(ualisation) in Documentation: Best Practices, Recommendations and Open Questions | Journal of Open Humanities Data

Editors’ Summary: This discussion paper provides multiple perspectives on data curation. It considers not just the work done in academia but also in community organizations, such as Cultural Heritage Institutions. The paper also provides data management recommendations and future questions. Specifically, the authors emphasize the importance of strong and consistent dataset documentation. This paper will […]

Editors' Choice

Editors’ Choice: Conversations about conversational code: on the collaborative critical code studies reading of ELIZA

Editors’ Summary: In “Conversations about conversational code,” Mark C. Marino and colleagues revisit Joseph Weizenbaum’s ELIZA through the recovered original source code, showing how close, collaborative code reading can reshape software history. Based on four years of interdisciplinary work, the article uncovers discrepancies between Weizenbaum’s published accounts and the actual MAD-SLIP implementation, including undocumented features, […]

Editors' Choice

Editors’ Choice: Most of the Renaissance Has Never Been Read. Source Library Is Opening It.

Editors’ Summary: This post shares the launch of the Source Library project earlier this month. Source Library, founded by Derek Lomas, digitizes Renaissance-era books originally written in Latin or other languages, translates them using AI translation tools, and presents them for free on their website. This project combines OCR and machine translation to increase accessibility […]

Editors' Choice

Editors’ Choice: The problem with evidence production on AI in education

Editors’ Summary: In this post, Ben Williamson examines the growing quality control and methodological rigor crisis within the field of Artificial Intelligence in Education (AIED) research. By highlighting the recent retraction of a high-profile paper on ChatGPT and analyzing two new critical literature reviews, Williamson demonstrates how the pressure to quickly produce statistical evidence has […]

Editors' Choice

Editors’ Choice: the friction embedded in AI educational designs

Editors’ Summary: In this post, Alex Reid critiques the reliance on instructional design and “design thinking” to counter the frictionless nature of AI in higher education. Challenging the popular notion of “engineering friction” into curriculum, he argues that reducing learning to predictable outcomes merely creates automated “work” that AI easily replicates. Reid contends that AI […]

Editors' Choice

Editors’ Choice: Speculative Recommendation: Reframing AI for Interpretive Practice in the Digital Humanities

Editors’ Summary: In this paper, River Rain and Houda Lamqaddam consider how recommender systems can be reframed as speculative tools for humanistic inquiry rather than commercial personalization. By demonstrating how a fine-tuned computer vision pipeline maps visual similarities across 2,341 animated films, they highlight how algorithmic proximity can trace artistic influence and macro-level aesthetic shifts. […]