The Business of Digital Humanities: Capitalism and Enlightenment
Bibliographic record
Abstract
Background: The Advanced Research Consortium (ARC)The Advanced Research Consortium (ARC) began in 2005 with the launch of the Networked Infrastructure for Nineteenth-century Electronic Scholarship (NINES), the brainchild of Jerome McGann and Bethany Nowviskie. Organized around literary and historical periods, ARC is comprised of the directors of online scholarly communities that peer review digital projects and aggregate metadata for peer reviewed and other collections into an online search portal. The five ARC search portals are NINES, 18thConnect, MESA or medieval, ModNets or modernism, and ReKN or Renaissance (the latter two are forthcoming). In this article, we will discuss partnerships that ARC has established with proprietary data companies and the possible benefits for scholars and libraries from the possibility of collaborating with companies – vendors that serve data to libraries. More important, I will argue that there is a terrible threat hanging over disciplines such as literary studies and that we need to become avid archive entrepreneurs.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".