Bibliographic record
Abstract
The chapter deals with fidelity of content, specifically concepts and register. I first discuss the querelle (‘dispute’) between those who favoured word-for-word translations and those who believed in updating or beautifying the ancient text for their contemporary audience, as captured in the phrase ‘les belles infidèles’, an approach which involves the notion of ‘compensation’. I then ask how translators tackle key concepts in Virgil’s oeuvre, such as the untranslatable pietas of the Aeneid , along with specific challenges that arise from Virgil’s Latin texts, such as puns and the incomplete lines. I investigate how translators attempt to match the various registers of the Eclogues , Georgics and Aeneid , then I consider the lens provided by the theoretical spectrum of domestication and foreignization, with examples including Aeneid translations in Italian, English, Romanian, German, Brazilian Portuguese and Russian, concluding with Chew’s uncategorizable Georgics.
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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 | 0.010 |
| 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 | 0.025 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".