Wareh, Patricia. Courteous Exchanges: Spenser’s and Shakespeare’s Gentle Dialogues with Readers and Audiences
Bibliographic record
Abstract
Patricia Wareh's new book does important and intriguing work both around questions of literary inheritance and around questions of generic convention.On one level, the book makes a strong claim for a deeper consideration of the debt that William Shakespeare's work owes to that of Edmund Spenser.On a more nuanced level, this study helps us see how "discourses of courtesy offered the overlapping groups of readers and playgoers a vocabulary for […] recognizing their power to determine a text's meaning, and for understanding the consequences of those decisions for their own social identities" (4).The book delivers adeptly on both these levels, offering new insights for scholars interested not only in these two authors but also in questions of early modern reading practices as well as the dynamics of courtesy.The volume's first chapter offers a new way to think about Castiglione's The Book of the Courtier, setting the groundwork for the lines of inquiry that Wareh will pursue in Spenser's and Shakespeare's works within subsequent chapters.Wareh rightly identifies "the theatricality of the courtier's self-presentation" as a "critical touchstone, " and the first chapter's discussion of this idea will be familiar to some readers (45).However, this early review of Castiglione's text and its implications for social behaviour nicely sets up Courteous Exchanges' original discussion regarding how Shakespeare and Spenser "critique the link between pedagogy and pleasure that Castiglione's dialogue sought to establish" (64).The second chapter, "Playing by the Rules?Pedagogies of Pleasure and Inset Audiences in Spenser's The Faerie Queene and Shakespeare's Love's Labour's Lost, " helps us see Wareh's innovative approach in full effect.As the title of the chapter suggests, the analysis shows how both writers trouble the connection between pleasure and pedagogy that is so central to Castiglione's model of the ideal courtier's education.Once the foundation for the study is established, Courteous Exchanges takes up perhaps expected pairings of texts but does something new with them.The third chapter turns our attention to the role of courtly rhetoric in Much Ado about Nothing.The discussion of this play shows the volume at its
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".