As Is the Generation of Leaves, So Are the Generations of Cows, Mice, and Gigolos
Bibliographic record
Abstract
Chapter 5 looks at passages of carpe diem within longer texts, such as satires of Horace and Juvenal, Petronius’ Satyrica and Vergil’s Georgics . As carpe diem poems are read and re-read, they become independent textual objects: they can be inserted just about anywhere but never lose their lyric splendour. Thus, Vergil applies the carpe diem motif to a context as humble as cattle-breeding, while both Seneca and Samuel Johnson ignore the context and treat this section as vatic wisdom. This chapter analyses how such excerpts relate to Latin satire, which bastardised other texts, to late antique anthologising, medieval florilegia, and early modern commonplace-books. The chapter also proposes a new model for understanding textual allusions and intertexts in classical literature. Finally, the chapter argues that clichés are important features of classical culture that are worthy of close study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".