Bibliographic record
Abstract
“Cultivating Heraldic Histories” argues that early modern English heraldry generated vibrant historiographic debates within and across multiple institutions and communities. Though sixteenth-century elites insisted that heraldic privilege was limited to the inherently noble, heraldry remained a flexible social sign system that represented the histories and contemporary identities of people across the social spectrum. By focusing on what I call heraldry’s "social life,” my project creates a rich picture of heraldry’s place in early modern prose and drama, and adds nuance to several sweeping narratives of prior literary and historical scholarship. Rather than treating coats of arms as medieval nostalgia and heraldic texts as historical background, this study reads textual, visual, and oral heraldic discourses as literary in their own right, suggesting they functioned as catalysts—not just symptoms—of social mobility in early modern English culture. The first chapter argues that the volatile atmosphere in and around the College of Arms during the late sixteenth and early seventeenth centuries gave rise to a unique body of heraldic writing by heralds and amateur writers. Although these books purported to decode heraldry’s relationship to English honor, they instead complicated the already labyrinthine discourses surrounding it. The second chapter uses archival sources to analyze an assortment of unofficial responses to printed heraldic texts. While heraldry officials published censures of each other’s work in print and manuscript, readers cross-referenced histories, disparaged court figures, and wrote mock-heraldic poetry satirizing their neighbors. Chapter three identifies the professional and economic relationships that linked heraldic performances at court to those in the popular theater. Populuxe dramas written by Kyd, Wilson, Middleton, and Shakespeare highlight the transactions underlying heraldic creation and display without undermining the system itself. The final chapter differentiates between English and Welsh historiographic modes in The Valiant Welshman, Shakespeare’s second tetralogy of history plays, and The Merry Wives of Windsor. Although Shakespeare’s plays acknowledge the importance of heraldry in English history, they depict the Welsh as creators of an inclusive British historiography founded on a multiplicity of narratives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".