Supplements and Paratextual Material
Bibliographic record
Abstract
Here I consider ways in which Virgil’s text is supplemented by translators. These supplements can take the form of translating additional material and of adding paratextual, explanatory material. Notable supplements considered include the spurious incipit of the Aeneid , the poems of the Appendix Vergiliana and the Latin supplement to the Aeneid written by Maffeo Vegio in 1428 which provides a happy conclusion to the poem. The paratextual material I consider consists of translator’s prefaces, notes and comments, along with issues raised by the cover, the title page, the dedication and endorsements, the mise-en-page , headings and illustrations, whereby the translator and/or printer attempts to frame and direct the reader’s experience. The presence or absence of the Latin text en face and the kind of annotation supplied raise the question of the intended uses of the translations. The chapter closes with a study of Douglas’ assertion of authorial presence through his paratexts.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.103 | 0.040 |
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".