Bibliographic record
Abstract
Martha Driver and her husband Tom Rhodes after her public lecture following the performance of Merry Wives of Windsor, Pace 2010. Dr. Martha W. Driver is Distinguished Professor of English and Women’s and Gender Studies at Pace University in New York City. A co-founder of the Early Book Society for the study of manuscripts and printing history, she writes about book production, illustration from manuscript to print, and the early history of publishing. In addition to publishing 50 articles in these areas, she has edited seventeen journals in fourteen years. These include: Film & History: Medieval Period in Film 29, 1-4 (1999); a special issue of Literary & Linguistic Computing, “Teaching the Middle Ages with Technology” 14, 2 (June 1999); and the Journal of the Early Book Society (from 1997 through 2011). Her books about pictures (from woodcuts to film) include The Image in Print: Book Illustration in Late Medieval England (London and Toronto: British Library Publications and University of Toronto, 2004); The Medieval Hero on Screen: Representations from Beowulf to Buffy, edited with Sid Ray (Jefferson, N.C., and London: McFarland, 2004); An Index of Images in English MSS, fascicle four, with Michael Orr (Turnhout, Belgium: Brepols, 2007); and Shakespeare and the Middle Ages, with Sid Ray (McFarland, 2009). She is a co-editor of the Texts and Transitions book series published by Brepols. For additional information click here
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.151 |
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".