A Computational Approach to Source Adaptation in Thomas Malory’s Morte Darthur
Bibliographic record
Abstract
The many source texts that late medieval adaptors like Thomas Malory worked with constitute a potential wealth of information concerning the genesis of the linguistic and stylistic features shaping their works. As a result, their texts provide a useful framework for further developing and testing methods of stylometric analysis in the context of adaptation as a collaborative form of authorship. Our interdisciplinary team has undertaken a stylometric analysis of the eight different sections of Thomas Malory’s Morte Darthur, in order to identify differences between these sections and how they correspond to the language of the Old French and Middle English sources that Malory is known to have worked with in the different sections of his work. Our findings provide a basis for addressing unresolved scholarly questions concerning Malory’s Morte, such as the nature of the source used for his “Tale of Sir Gareth” and whether Malory himself was responsible for the differences between the two surviving versions of his “Roman War” episode in Book II of the Morte. They further shed light on ongoing controversies concerning the overall textual unity of the Morte and the process by which Malory created the different sections of his work—an issue that lies at the heart of broader debates concerning where Malory and other late medieval adaptors are to be situated on the continuum between “faithful translator” and “original author.”
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".