01
The tools came long before this AI wave
Digital humanities did not wait for chatbots to invent mediation. In 1949, Roberto Busa’s Aquinas project already meant IBM, punch cards, operators, and years of checking.DHQ’s reconstructionmakes the human work inside that automation hard to miss. Later, TEI put shared encoding under community rules. The web turned archives into interfaces people could click.
ORBISturned an argument about Roman movement into a network model you could argue with. TheLDA special issuewas my attempt to make the old collaboration problem concrete: put an imported model beside its makers, users, critics, and tools. None of that reduced mediation. It moved the boundary between interpretation and implementation.
AI-assisted coding moves that boundary again. A scholar can now ask for a transformation, an interface, and a chart while the research question is still half-formed. That is more than “another tool,” and less than magic. The tool did not disappear. The person with the question can generate more of the software.
ShowsThirteen documented changes in how ideas become software.Does not showA story of smooth progress, or equal labor in every project.
The interface to implementation moves from punch cards and governed markup to graphical tools, code education, natural-language code generation, and repository agents. Authority moves but never vanishes.
02
Editors group the field before you read it
DHQ reports 710 peer-reviewed articles and case studies through 2025: 386 in the regular stream and 324 in special issues. Those routes have different histories, so they are not simply competing acceptance rates.
In this 806-item public corpus, 353 items sit inside 38 named clusters. A cluster gathers work under a shared question before a reader, or a recommendation system, ever arrives.
A spike in a subject can mean the field got busier there. It can also mean editors invited that conversation. Building a new app does not undo the earlier decision about what enters the reading room together.
ShowsWhere published items sit in public named clusters.Does not showWhy a paper was accepted, or comparable acceptance rates by route.
353 items are placed in 38 named public clusters. Placement records an editorial grouping, not a reason for acceptance or a special-issue submission flag.
03
More names on the byline
The standard AI demo is solitary: one person, one prompt, one finished app. DHQ’s published record was already moving the other way before the present AI wave.
Items with two or more listed authors rise from 32.1% in 2007–11 to 54.7% in 2022–25. The line jumps year to year because the journal published between 6 and 79 items annually. Small denominators make every year loud.
A byline is a narrow record. It does not say who wrote code, cleaned data, designed an interface, found funding, or kept a server alive. It only says that named coauthorship became more common.
ShowsHow often published items list one name or several.Does not showWho did what labor, or why teams formed.
The chart describes listed bylines only. It does not resolve people or infer roles, labor, or reasons for collaboration.
04
What DHQ calls its subjects now
In the current XML, media studies sits on 36.7% of items published in 2007–11 and 3.4% in 2022–25. History rises from 15.6% to 21.2%. Race rises from 0.9% to 14.4%. Project report peaks at 26.7% in 2017–21.
These are DHQ’s controlled tags, not topics guessed by a model. An item can carry several tags at once, so the bars are not a pie that must sum to 100. The vocabulary is inspectable, which is firmer ground than a free-form topic cloud.
One bar is so extreme that it deserves its own question. Media Studies begins as DHQ’s most common controlled category. What does it mean for that category to nearly disappear?
ShowsHow often current controlled tags appear by publication window.Does not showMutually exclusive topics, field-wide prevalence, or labels assigned at publication time.
The bars describe the pinned archive with its current vocabulary. Retrospective keywording means they are not a contemporaneous topic series.
05
DHQ stopped treating the digital as a medium
Media Studies appears on 40 of 109 items from 2007–11 (36.7%), then on just 8 of 236 from 2022–25 (3.4%). A conservative family of narrower media tags briefly absorbs some of the difference, but the whole explicit media ecology still contracts from 45.0% to 13.1%.
The deeper change is what Media Studies no longer connects. Early on, 6 of 14 tools articles and 11 of 27 articles tagged “DH” also carried Media Studies. In the latest window those overlaps are both zero. Its overlap with project reports falls from 6 of 19 to 1 of 40; with cultural criticism, from 7 of 17 to zero of 21.
Early DHQ used Media Studies as an umbrella for the field’s own technical condition: interfaces, electronic publishing, collaboration, tools, and the strangeness of digital form. Later DHQ still makes and studies digital systems, but files them as methods, disciplinary applications, projects, and politics. The journal did not stop being digital. The digital stopped being the shared object that needed explaining.
ShowsHow Media Studies retreats overall and from four categories it once connected.Does not showUnlabeled media analysis, full-text topics, or the whole field of digital humanities.
40 opening-window items carry Media Studies, compared with 8 in the latest. Within Tools the overlap falls from 42.9% to zero, and within Digital Humanities from 40.7% to zero.
06
The tools tag is not where all the tools are
The explicit tools category does not trace a simple fall. It marks 12.8% of the opening window, rises to 24.0% in 2017–21, and returns to 13.6% in 2022–25. Project reports follow a similar arc. An item carrying either tag accounts for 27.5%, 27.2%, 44.1%, and 26.3% of the four windows.
DHQ defines tools as work about platforms, apps, workflows, tool criticism, presentation, review, or adoption. That is a category of discourse, not an inventory of every article that computes. The taxonomy itself says tools is often linked to project reports, which is why the union is a better floor for visible making than tools alone.
The elision becomes stark in 2024–25. 28 of 91 items carry machine learning, NLP, data analytics, or data visualization; only 1 of those is tagged tools. All 13 recent formal case studies are practical work, and none is tagged tools. Practice became method, case, and situated intervention rather than “here is a tool.”
ShowsHow explicit tool discourse relates to DHQ’s own project-report category.Does not showEvery computational method, software dependency, or kind of practical labor.
Explicit tools peaks in 2017–21 rather than declining steadily. The tools-or-project union covers 27.5% of the opening window and 26.3% of the latest.
07
A turn toward consequence but not away from making
Compare the opening and latest windows and the largest gains are not a roll call of traditional disciplines. Race rises from 0.9% to 14.4%. Ethics goes from zero to 11.9%. Minimal computing goes from zero to 7.6%. Social justice, global DH, archives, and gender all gain ground; history is the major conventional field among the eight.
Meanwhile Literary Studies falls from 23.9% to 12.3%, and the old connective vocabulary also recedes: collaboration, infrastructure, publishing, and information retrieval. So “more humanities” is only half right. DHQ became less preoccupied with naming digital mediation and more preoccupied with whom digital work serves, where it happens, what it costs, and what it does to its subjects.
The journal’s founding question was how to shape digital humanities. Its currentcommunity statementmakes the desired shape explicit: inclusive, global, equitable, accessible, and attentive to the labor that sustains scholarly community. The tools remain. Their obligations moved to the foreground.
ShowsThe eight largest percentage-point gains from the first to latest window.Does not showWhy the changes occurred, editorial causation, or a field-wide topic model.
Race gains 13.5 percentage points and Ethics gains 11.9. The leaders emphasize power, responsibility, limits, and situated practice.
08
AI fits the methods and collides with the mythology
DHQ already shows us how the present field meets AI. Its2023 volume 17.2includes a named “Code Legibility and Critical AI” section. Across the issue’s 26 items, tools and code studies appear nine times each, cultural criticism four times, machine learning three times, and Media Studies not once. AI is not rejected as computation. It is read as code, method, opacity, bias, gender, and politics.
In the earlier shape of DH, AI-assisted coding would have looked like a long-awaited redistribution of implementation power. The humanist can make weird software without first submitting the question to a programmer’s veto. That is genuinely decolonizing along one axis. But the 2011 argument also objected to imported tools that shrink rich questions to fit conventional software. A model trained on conventional code can automate that contraction at extraordinary scale.
Current DH adds a harder test. DHQ’sAI policypermits supportive use, but keeps direct agency, disclosure, accuracy, and responsibility with human authors. AI conflicts with today’s DH not because the field stopped computing, but because the product mythology of effortless substitution collides with a field now organized around situated labor and human scholarly relations.
So yes: AI can be a decolonial tool. Cheap code is not decolonization by itself. The claim becomes true when access, language, ownership, governance, community, and consequences change with it.
ShowsHow one issue containing an explicit Critical AI section was tagged.Does not showEach article’s stance, all DHQ writing about AI, or consensus across the field.
9 items are tagged Tools and the same number Code Studies; 3 are tagged Machine Learning and 0 Media Studies.