Bibliographic record
Abstract
The digital humanities is increasingly becoming a buzzword, and there is more and more talk about a broadly conceived, inclusive digital humanities. The field is expanding and at the same time being negotiated, and this article explores the idea of a broadly conceived landscape of digital humanities in some depth. It is argued that awareness across this landscape is important to the future of the field. The study starts out from typologies of digital humanities, a flythrough of the landscape, and a discussion of what being a digital humanist entails. The second part is an exploration of four concrete encounters: ACTLab at University of Texas at Austin, the Humanities Arts Science Technology Advanced Collaboratory (HASTAC), the Humanities Computing Program at the University of Alberta, and Internet Studies. In the third part of the article, it is suggested that a model based on paradigmatic modes of engagement between the humanities and information technology can help chart and understand the digital humanities. The modes of engagement analyzed are technology as a tool, study object, expressive medium, exploratory laboratory and activist venue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.033 | 0.026 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".