Bibliographic record
Abstract
Though John Skelton is commonly regarded as a quintessentially English poet, one of his earliest appearances in the literary record (in 1490) involves him with traditions of classical writing and with the act of translation – both narrowly, in the sense of rendering classical texts into English, and more generally, with the ‘carrying over’ of classical culture into England. These associations are significant, and in one way or another would stay with him, in terms mainly of Latin literature, for the rest of his life (Carlson 2016). William Caxton, who had been translating and printing since about 1473/4, expresses some perplexity as to what linguistic register to choose for his English version of the Aeneid , based on a French translation that he had among his papers. What attracted him to the French version were ‘the fayr and honest termes and wordes in Frenshe, whyche I never sawe tofore lyke ne none so playsaunt ne so wel ordred’ (Caxton 1973: 78–81). He thought that a translation would appeal to ‘noble men … as wel for the eloquence as the hystoryes’, but when he looked over the first few pages of his version he became aware that he had adopted some of the ornate vocabulary from the French – he calls them ‘fayr and straunge termes’ – and realised that this gave him a problem with his potential readers: ‘I doubted that it sholde not please some gentylmen whiche late blamed me saying that in my translacyons I had over-curyous termes whiche coude not be understand of comyn peple and desired me to use olde and homely termes in my translacyons.’ Caxton, dutifully, looked at older documents but ‘the Englysshe was so rude and brood that I coude not wel understand it’. It was ‘more lyke to Dutche [German] than Englysshe’. In about 1505, Skelton, through the persona of Jane Scrope, expresses something of the same disquiet about English: ‘Our natural tong is rude’, she says, and she uses adjectives such as ‘rusty’, ‘cankered’ and ‘dull’ to describe it. She says that if she were to attempt to write ‘ornatly’ she would not know where to find ‘termes to serve my mynd’ – because she has no knowledge of classical writing: ‘These poetes of auncynte …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.122 | 0.050 |
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".