Bibliographic record
Abstract
Articles of clothing are amongst the most frequently named objects in extant comedies and fragments. Stage costume serves as a narrative device, and a visual signifier of character and status. Because garments worn by actors were in many respects the same as everyday dress, Greek audiences read their subtle codes with ease, but a modern audience or costume designer may be baffled by terminology. ‘Where did you get that thoimation ?’ asks an Informer. ‘Yesterday I saw you wearing a tribonion ’ ( Wealth 881–2). The Informer's words are a verbal stage direction, prescribing what the Honest Man must wear in an earlier scene as well as in this one. Evidently he has changed his clothes, but what sort of change is implied? Have overalls replaced his Sunday suit? If a gloss explains that he has exchanged a homespun cloak for a woollen one, we may understand that his fortunes have improved, but we may not be able to visualize both garments. When we turn to images the Greeks made of comedies in action, we are inevitably puzzled. Much of our evidence for comedy in the classical age is visual, designed to communicate within a specific culture, time and place. If we are to understand comedy, we must attempt to decode its visual language. Old and Middle Comedy flourished longer than modern Greece has been a nation. Beginning as an amateur choral event on the fringe of an Athenian festival, it was gradually transformed into a narrative performed by transnational celebrities. It would be strange if costume conventions failed to evolve. Fortunately, visual evidence is now abundant enough to permit us to glimpse some of the earliest comic costumes, and to follow developments during much of the classical period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".