Skelton’s English Diodorus and the 1481 Edition of Poggio’s Latin Translation
Bibliographic record
Abstract
Evidence is that, for his English translation, the Tudor laureate John Skelton (c. 1460–1529) more likely used the Venice, 1481, edition of the Florentine eminence Poggio Bracciolini's Latin translation (1449) of the Greek universal historian Diodorus Siculus (fl. 60–30 BCE), rather than any of the other printed editions available by the date. A terminus ante quem for Skelton’s version is supplied by William Caxton’s reference to it in the prologue of his own Eneydos, printed in 1490, where Skelton’s Diodorus is mentioned in company of a Cicero-translation also said to be the laureate’s doing: ‘For he hath late translated the epystlys of Tulle and the boke of Dyodorus Syculus’.1 Moreover, the auto-encomiastic sections of Skelton’s later Garland of Laurel2 claim the same two translations, both the ‘Diodorus Siculus of my translacyon | Out of fresshe Latine into owre Englysshe playne’ (Garland 1498–99), as well as the Cicero, although, unlike Caxton’s phrase, ‘the epystlys of Tulle’, Skelton’s reference, scholarly-precise, specifies which of the collections of Cicero’s epistolography he had translated, namely, the Ad familiares: ‘Of Tullis Familiars the translacyoun’ (Garland 1185). Both translations, predating Caxton’s reference, probably also predate Skelton’s entry into royal service in 1488, when he began dating his writings by means of his idiosyncratic annual-calendar.3 Use of the calendar does not occur in the surviving Diodorus-version, which may well be as early as the early to mid-fourteen-eighties, when Skelton was a working grammarian, nearer his student-days.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".