‘This Rude Laboure’: Lord Berners' Translation Methods and Prose Style in <i>Castell of Love</i>
Bibliographic record
Abstract
In his prologue to Castell of Love, Lord Berners claims to have translated the romance ‘out of Spanyshe in to Englyshe’ (A2r).1 This declaration is echoed by the title pages to all three early printed editions (?1548, ?1552, and c. 1555), which advertise the volume as ‘The Castell of Love, translated out of Spanishe in to Englyshe by Johan Bowrchier Knyght, Lorde Bernis’ (A1r). Though Berners maintains that he translated Diego de San Pedro’s Carcel de amor directly from the Spanish, his assertion is not completely accurate, as his translation is marked by close verbal parallels to the French translation of San Pedro’s text. Moreover, the French prologue differs completely from the Spanish, and Berners’ prologue matches the French word for word, sharing no variant readings with the Spanish prologue. Part of Berners’ Castell, however, is definitely translated directly from Spanish: it includes the Spanish continuation written by Nicolas Nunez, which was not included in any French translation prior to 1533, the year of Berners’ death. The continuation is far from the only instance where Berners uses the Spanish original; traces of indebtedness to the Spanish text can be found throughout Castell. My comparison of the three versions (English, French, and Spanish) indicates that, in the English translation, many Spanish readings are retained and prioritized over the French variants, demonstrating a reliance on the Spanish text, and that equally many French additions, omissions, and alterations are
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".