Poetic Careers of Virgil Translators
Bibliographic record
Abstract
I consider the position of Aeneid translations in the career patterns of a spectrum of poets and scholars in a range of languages, with attention to those who tackle other high-prestige texts, such as the Homeric epics, Ovid’s Metamorphoses and Dante’s Divine Comedy. I ask whether the Virgil translation was the chef d’œuvre or an apprenticeship, whether the sequence of translating had any impact on the translator’s other output, and what difference this makes to our reading of the Aeneid translations. After highlighting some of the issues via Harington, whose Ariosto translation influenced his Aeneid translation, I analyse the synergy between Dante and Virgil in Villena’s Castilian translations. Most of the chapter deals with Virgil translators who also translated Homer, including Mandelbaum, Fitzgerald, Lombardo and Fagles, with longer discussions of Ogilby, Dryden and Morris. I close with an examination of Day-Lewis who translated the Georgics first, then the Aeneid and finally the Eclogues .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".