Bibliographic record
Abstract
Digital humanities has a Shakespeare problem; or, to frame it more broadly, a canon problem. This essay begins by demonstrating why we need to consider Shakespeare’s position in the digital landscape, recognizing that Shakespeare’s prominence in digital sources stems from his cultural prominence. I describe the Shakespeare/not Shakespeare divide in digital humanities projects and then turn to digital editions to demonstrate how Shakespeare’s texts are treated differently from his contemporaries—and often isolated by virtue of being placed alone on their pedestal. In the final section, I explore the implications of Shakespeare’s popularity to digital humanities projects, some of which exist solely because of Shakespeare’s status. Shakespeare’s centrality to the canon of digital humanities reflects his reputation in wider spheres such as education and the arts. No digital project will offer a complete, unmediated view of the past, or, indeed, the present. Ultimately, each project implies an argument about the status of Shakespeare, and we—as Shakespeareans, early modernists, digital humanists, humanists, and scholars—must determine what arguments we find persuasive and what arguments we want to make with the new projects we design and implement.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".