Bibliographic record
Abstract
Comedy is notoriously resistant to theorization. There is, after all, something inescapably comic and self-defeating about the scholar, oblivious to comedy's charms, searching out its origins or trying to account for its effects. In Cicero's De Oratore , one of the interlocutors in the discussion of the comic notes that everyone “who tried to teach anything like a theory or art of this matter proved themselves so conspicuously silly that their very silliness is the only laughable thing about them.” Small wonder then, that at the conclusion of Umberto Eco's The Name of the Rose the sole manuscript of Aristotle's treatise on comedy, the counterpart to his discussion of tragedy in The Poetics , should perish and a fire destroy the monastery library in which the corpus of classical learning has been preserved. But the situation is, of course, more complicated than Eco's fable suggests, both because of widely known alternate accounts of comedy in the classical tradition and of the presence of the outlines of a theory of the genre in The Poetics itself. Any discussion of theories of comedy in the Renaissance will inevitably emphasize the importance of these resources in sixteenth-century discussions of the issue. This approach runs certain risks: there were, after all, sometimes divergent conceptions of comedy in the period. Moreover, Shakespeare’s comedies in particular resist theoretical and generic pigeonholing.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".