Exploring the Digital Humanities: An Interview with Mark Algee-Hewitt, Ph.D.
Bibliographic record
Abstract
Mark Algee-Hewitt is an Assistant Professor in the department of English and the Co-Director of the Stanford Literary Lab. His work focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts. In particular, he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophic concepts during the Enlightenment and Romantic periods. He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century. At the Literary Lab, Dr. Algee-Hewitt leads projects on suspense literature, the relationship between titles and texts in the long eighteenth century, and gender performance in the dialogue of novels written during the Romantic period. He is also a collaborator on the Canon/Archive project, Micromegas, the Transhistorical Poetry project, Modeling Dramatic Networks, and a project on the Supreme Court and Environmental Law. Outside of Stanford, Dr. Algee-Hewitt is a partner in the ongoing NovelTM partnership grant and is an associate principal investigator of the Stanford branch of the Global Currents Digging into Data project. Building on this work, he has ongoing collaborations with Andrew Piper at the .txt lab at McGill University in Montreal, and with the North American Concept Lab, based at New York University. He is also a member of the executive board of 18thConnect and is on the visualization advisory committee of the Digital Mitford project.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".