Bibliographic record
Abstract
To shed light on the pedagogic interests of the sentential, Seneca often alludes to the mime, and particularly to Publiulius Syrus, whose sentential had been gathered early in an anthology and were remarkably renowned in Antiquity. However, the whole corpus of the fragments of mime deserves our attention in this respect, especially the words of Laberius. Grammarians and erudites have also extracted sentential, gnomai, and general reflections from other contemporary comic genres, notably the fabula togata and the fabula Atellana, also transmitted in a fragmentary state. Analysing the sentential and the gnomai of the mime through their reception in other texts, this paper will attempt to determine their specificity: did the grammarians, or Aulus-Gellus and Macrobius, and even Augustine, prize the sentential which came from the mime? Was it felt as a specific type of quotation? And how can we situate the position of Seneca's reading of the sentential in this context? These questions lead us to a crucial point: how did the mime-readers receive this dramatic form in Antiquity? As a literary genre in its own right, using sentential and gnomai with the same judgment and efficiency as other dramatic genres? As an ambiguous form combining serious thought and coarseness, or an exclusively "popular" practice in which the gnomic phrases played a specific role?
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".