Bibliographic record
Abstract
Drama is generally taken as a representative art: it is not life, but it represents life. It is the art of actors pretending to be something that they are not. Rhetoric is generally taken as the art of using words to persuade and manipulate, irrespective of the truth, essence, and sincerity; it is the art of pretense and the constructed truth. In Rome, in particular, it is primarily the art of lawyers who represent and advocate for their clients, speaking for them, even acting out emotions that their clients are presumed to feel. It is an accident of language that both the actor and the lawyer are called actores (after all, herdsmen, bailiffs, and artillery men are also actores ): the term refers to the one who performs the material task (as opposed to, say, a producer). But the accident underscores similarities: the performances of actores as representatives of others (characters or clients) are artistic, manipulative, insincere. They are the ones who hide behind the mask, manipulate the mask, make you see what you do not see. But orators also represent themselves, their auctoritas (personal authority and influence) and their character. The courtroom is the stage where they assume the persona of the uir bonus (the good or manly man). One version of the uir bonus was the sincere speaker, the man of no artifice. But how do you act like uir bonus ? Cato the Censor, a man who inveighed against Greek rhetoric and influence and had the orators tossed out of Rome, reduced rhetoric to a simple formula: tene rem, uerba sequentur ! (“Stick to the truth; the words will follow.”) But Cato himself studied rhetoric and had his sons learn Greek.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".