Bibliographic record
Abstract
Marcus Valerius Martialis (c.40-104 CE) was one of the wittiest and dirtiest of the Latin poets. Through eleven of his fourteen books of epigrams wit, poetic invention, and suggestive or, more often, explicit sexual situations come together in virtually every imaginable combination. There are excruciatingly elaborate comparisons of anatomy to various daily and not-so-daily objects (“Lydia’s beaver is as loose as a horse’s rear, as a swift-spinning bronze hoop, as the wagon-wheel through which the acrobat leaps…they say that I fucked Lydia in a pool: I can’t be sure; I think I fucked the pool” Book XI, 21), comic exaggerations (“As huge as your cock is, Papylus, your nose is just as long, so that you get a nasty snort of something whenever you get hard” Book VI, 36), and calumnies of every description (“Zoilus, you have fouled the water by washing your arse in it: the only way it could be worse is if you washed your head in it” Book II, 42). All of this makes Martial an ideal place to start when trying to work out the links between humour and sexuality. What is it precisely about sexuality that makes it useful to Martial? What does it have in common with other forms of humour? Are there patterns to how Martial employs his bawdiness?This paper explores these questions through a careful examination of three of Martial’s poems. In order of ascending bawdiness, they are: Book II, 52 (Novit loturos Dasius…), Book III, 26 (Praedia solus habes…), and Book XI, 21 (Lydia tam laxa est…). I argue for an understanding of humour as the satisfaction of a pattern or expectation in an unusual or surprising manner. Going through each poem in turn, I discuss the patterns or expectations Martial evokes in each and how they are satisfied. Finally, I argue that bawdiness is often a source of humour because we have a large and vivid set of expectations when it comes to sexual matters – expectations which can be satisfied in all manner of unexpected ways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".