How to do things with Shakespeare: new approaches, new essays
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
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".