Bibliographic record
Abstract
LIZA BLAKE is Associate Professor of English at the University of Toronto, with research interests in the intersection of literature, philosophy, and science; women writers; textual editing; and book history. Her work is published and forthcoming in SEL, ELR, PBSA, JEMCS, Criticism, and postmedieval. She has edited Margaret Cavendish’s Poems and Fancies: A Digital Critical Edition (https://library2.utm.utoronto.ca/poemsandfancies/), and she is one of the General Editors of The Complete Works of Margaret Cavendish, under contract with Punctum Books. KATHERINE GILLEN is Associate Professor of English at Texas A&M University–San Antonio and author of Chaste Value: Economic Crisis, Female Chastity and the Production of Social Difference on Shakespeare’s Stage (Edinburgh University Press, 2017). She is currently working on a monograph exploring intersections of classicism and racial whiteness in early modern drama, and she is coediting an anthology of Shakespeare appropriations set and staged in...
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.481 | 0.266 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".