Bibliographic record
Abstract
Introduction While digital humanities has grown, so too has the number of voices making the case for attention to race, class, gender, sexuality, ability, nationality, and other categories of identity in the field. Increasing numbers of panels at the annual meetings of Digital Humanities; Modern Language Association; American Studies Association; American Historical Association; National Women's Studies Association; and Humanities, Arts, Science, and Technology Alliance and Collaboratory (HASTAC) examine the role of difference in digital humanities scholarship. In today's “digital humanities moment,” the field often reencounters the growing pains of the “eternal September of the digital humanities.” As a result, recurring questions insist on the need for cultural critique in the field: “Where is cultural criticism in the digital humanities?,” “Can we describe digital archives as feminist?,” “Why are the digital humanities so white?,” and “Can information be unfettered?” The persistence of these questions demonstrates the need for more answers to the pressing matter of inclusion and exclusion within the field. A recent special issue of the journal differences , “In the Shadow of the Digital Humanities,” considers the fraught relationship between digital humanities and diversity. The call for papers for the Canadian Society for Digital Humanities and Association for Computers and the Humanities joint conference encourages proposals from “women, people of colour, LGBTQ, or other underrepresented groups.” The 2015 Digital Diversity conference in Edmonton celebrates the twentieth anniversary of The Orlando Project and asks, “Have decades of digital studies enhanced, altered, or muted the project to recover and represent more diverse histories of writers, thinkers, and artists positioned differently by gender, race, ethnicity, sexualities, social class, and/ or global location?” Such calls suggest that scholars within digital humanities have begun recognizing the need for inclusive representation and a critical approach that foregrounds intellectual diversity within the field. Resistance to the utility of cultural criticism abounds. Notably, Matthew Kirschenbaum argues many critics target a construct of “digital humanities” rather than the varied range of projects that comprise the field. In distinguishing between a discursive subject of criticism and material praxis, he echoes debates over the “hack vs. yack” binary— doing vs. theorizing— that have taken place in the field.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.018 | 0.080 |
| Scholarly communication | 0.036 | 0.038 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 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".