Competition, Retranslation and Travesty
Bibliographic record
Abstract
The topic of competition starts with translators’ incorporation of others’ versions into their own texts, then moves on to translators’ prefaces where they situate themselves in relation to particular predecessors, such as Leopardi’s relationship with Caro’s sixteenth-century Eneide . I examine in depth the self-positioning and self-fashioning in the paratexts in the English tradition of Aeneid translations from Caxton down to Wordsworth. The second section deals with the phenomenon of ‘retranslation’, which has two manifestations: when translators lift elements from preceding translations and when they revisit their own earlier versions and modify them. Then I consider competition with Virgil himself, starting with the challenge to Paul Valéry to translate the Eclogues . The chapter concludes with brief consideration of parody and travesty of Virgil as special forms of retranslation, with examples from a seventeenth-century Dutch collaboration on the Eclogues , a seventeenth-century parody of Eclogue 1 by an Irishman and an eighteenth-century travesty of the Aeneid in German.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".