The Influence of Dominic Mancini’s Sympathetic Chronicle in Shakespeare’s Richard III
Bibliographic record
Abstract
My research looks at Shakespeare’s unsympathetic representation of King Richard III in The Tragedy of Richard III. Shakespeare was not the first to present Richard negatively: sixteenth-century chroniclers such as Robert Fabyan, Polydore Vergil, and especially Thomas More played a significant role in scripting the Tudor Myth that portrayed Richard as a corrupt, disfigured monarch. In my research I have located a chronicle written during Richard’s reign, Dominic Mancini’s 1484 chronicle The Usurpation of King Richard III, which reports favourably of the king. I will show that Shakespeare incorporates events from Mancini’s chronicle, though reshapes that material to support and advance the Tudor bias against the last Yorkist King. In Mancini’s chronicle, for instance, Elizabeth Woodville persuades her husband Edward IV to have his brother Clarence murdered in the Tower; in Act One of Shakespeare’s Richard III, Richard schemes his way to the crown by creating a false prophesy that Edward uses as grounds to murder Clarence. In Shakespeare’s Richard III, Richard uses his physical deformity and his inability to “prove a lover” (I.i.28) to justify his desire to “prove a villain” (I.i.30); Mancini records no evidence or testimony concerning Richard’s body, and the recent 2012 exhumation of Richard’s remains shows no sign of physical deformity other than a slight spinal curvature. Overall this presentation aims to reconstruct the way modern readers and audiences view Richard III, and to question the role that significant literary texts play in reshaping historical 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".