Illuminating a Fall of Princes Fragment: A Study of Manuscript Production and Reader Reception
Bibliographic record
Abstract
This thesis investigates the production and lives of two late medieval manuscripts containing John Lydgate's Fall of Princes: Montreal, McGill University Rare Books and Special Collections, MS 143 and London, British Library, Harley MS 1766. These illuminated manuscripts likely originated from the same workshop and are the only surviving witnesses to a specific version of The Fall. By placing McGill and Harley into conversation, I clarify that the way in which each manuscript uniquely presents this recension results in diverse reader interpretations. As I focus on the McGill fragment and its production process, I present a detailed description that examines the textual and non-textual manuscript components of McGill and, to a lesser extent, Harley. My description includes the two surviving McGill illustrations, one of which is a presentation scene. The manuscripts’ political and cultural contexts are then assessed within a framework informed by late-medieval representations of authorship and art history. I establish that the McGill miniatures were completed decades after those in Harley, demonstrating that analysis of political motivations behind the recension in Harley cannot be applied to McGill. Furthermore, I show how mid fifteenth-century intentions behind the presentation scene may have shifted by the late fifteenth century from a focus on the individual figures in the portrait to a focus on patronage
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".