Freebury-Jones, Darren. Reading Robert Greene: Recovering Shakespeare’s Rival
Bibliographic record
Abstract
Collaboration, revision, influence, competition, and rivalry were the most frequent and the most fruitful kinds of interaction among playwrights working in the burgeoning theatre scene in late Elizabethan London.In a series of influential articles preceding the publication of Reading Robert Greene: Recovering Shakespeare's Rival, Darren Freebury-Jones has already made a significant contribution to the study of influence, authorship, and chronology of the plays by Shakespeare and of his contemporaries.This book applies Freebury-Jones's method to an author who has waited too long for just this kind of comprehensive revisionary analysis and chronology of his plays.He compares marginal, collaborative, and acknowledged plays by Robert Greene with each other and across these three categories to reveal "a network of interrelations" (5) between Greene's plays and the plays of some of his contemporaries.The book is impeccably researched and concisely written.It makes a clear argument about stylistic versatility of Greene's drama, his interaction with Shakespeare and other playwrights, and about the significance of his dramaturgy and style in the context of other drama of the late Elizabethan period.It also restores the canon of Greene's drama and revises its chronology.Freebury-Jones's method enhances both quantitative data and qualitative conclusions produced by his predecessors as critics by his own philological method of assembling and organizing verbal evidence.The book's two appendices containing the verbal data arising from his research are the best tools for further research of the style and the verbal idiom of Greene's plays.Freebury-Jones is a sensitive reader of both the smallest units of verbal utterance and of the larger thematic concerns used by the playwright.The book is organized in seven chapters.The first chapter situates Greene's canon in the context of "commercial rivalry" (16) in London theatres.Building on the work of theatre historians, Freebury-Jones maps out the literary, dramatic, and stylistic aspect of the plays that provided the background for Greene's independent and collaborative responses.From the outset of his career, Green proved to be a talented competitor, able to "create new plays within extant genres" (21), especially romance, and keen to experiment with
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.021 |
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".