Bibliographic record
Abstract
Here I examine the phenomenon of partial as contrasted with complete translations: some translators publish complete translations of the Aeneid and the Eclogues , while others select individual books or poems. Some selections, for example Eclogue 4 and Books 2 and 4 of the Aeneid , are consistently popular, while others wax and wane. My chief focus is on partial translations of the Aeneid , where the ability to select and isolate individual books or passages gave translators great flexibility and the freedom to domesticate the material or to turn it to particular aims. After a glance at the ‘Messianic’ Eclogue ( Eclogue 4) and the ‘Aristaeus epyllion’ (from Georgics 4), I analyse some famous and less famous translations of Books 4, 2, 1 and 6. Factors that explain some of these selections include the translator’s self-image, education and circumstances, their aims and ambitions, and their motivations for writing as generated by patronage and the venue of publication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".