Bibliographic record
Abstract
Stephen Gosson might treble and intensify his famous antitheatrical malediction could he know what a cliché of theatre history one sentence of it has become: I have seen it that the Palace of Pleasure , the Golden Ass , the Ethiopian History , Amadis of France , the Round Table , bawdy comedies in Latin, French, Italian and Spanish have been thoroughly ransacked to furnish the playhouses in London. It is time to take Gosson seriously, to identify Shakespeare as one of the ransackers and to treat his Italian stories as a chapter in the history of ransacking, which also entails treating ransacking itself as a first premise of Renaissance dramaturgy. Ever since Chaucer’s Clerk and Franklin told tales from the Decameron , English literature has borne traces of Italian stories, though to call them “Italian” is to dismiss their remote origins, in many cases lost in the distance of antiquity and Indo-European folklore. It was the Renaissance versions, however, the “mery bookes of Italie ” that delighted sixteenth-century English readers and, according to Roger Ascham, undermined their faith and morals. Playwrights in those times before copyright laws were under no pressure to invent original stories and instead valued new presentation of old material. Italy was the contemporary crucible of dramatic theory and Tasso, foremost among theorists, wrote that originality in dramatic composition should consist in form rather than in matter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 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".