How to do things with Shakespeare: new approaches, new essays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Notes on Contributors. Introduction: Laurie E. Maguire (Magdalen College, University of Oxford). Part I How To Do Things with Sources. 1. French Connections: Je-Ne-Sais-Quoi in Montaigne and Shakespeare: Richard Scholar (Oriel College, Oxford). 2. Romancing the Greeks: Cymbeline's Genres and Models: Tanya Pollard (Brooklyn College, City University of New York). 3. How the Renaissance (Mis)Used Sources: Art of Misquotation: Julie Maxwell (Lucy Cavendish College, Cambridge). Part II How To Do Things with History. 4. Henry VIII, or All is True: Shakespeare's Favorite Play: Chris R. Kyle (Syracuse University). 5. Catholicism and Conversion in Love's Labour's Lost: Gillian Woods (Wadham College, Oxford). Part III How To Do Things with Texts. 6. Watching as Reading: Audience and Written Text in Shakespeare's Playhouse: Tiffany Stern (University College, Oxford). 7. What Do Editors Do and Why Does It Matter?: Anthony B. Dawson (University of British Columbia). Part IV How To Do Things with Animals. 8. The dog is himself: Humans, Animals, and Self-Control in Two Gentlemen of Verona: Erica Fudge. (Middlesex University). 9. Sheepishness in Winter's Tale: Paul Yachnin (McGill University). Part V How To Do Things with Posterity. 10. Time and the Nature of Sequence in Shakespeare's Sonnets: In sequent toil all forwards do contend: Georgia Brown (independent scholar). 11. Canons and Cultures: Is Shakespeare Universal? : A. E. B. Coldiron (Florida State University). 12. Freezing the Snowman: (How) Can We Do Performance Criticism?: Emma Smith (Hertford College, Oxford). Index
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it