Bibliographic record
Abstract
It’s too bad that the people who know how to ruin the country are busy teaching school. (Naff bumper sticker) Performance Accustomed to public speaking as all our companions are, they have none of them told us if they trained and how, pebbles in Demosthenic cheeks above the roar of the surf, or mantras intoned at some imaginary Archias’ feet. They have indeed regaled us with precious little of the mythology surrounding the star speakers of antiquity (with the necessary exception of Penner and Vander Stichele’s Jesus, Paul, and co.). And they have suppressed detail on their own investment in rhetoric, ancient or modern (with the exception of Heath’s adversion to his own hands-on teaching of ancient rhetoric in school). But in performative terms, the rest of these scholar essays practiced house-style exegetics even while preaching the intrication of rhetoric with form-content fusion and fission – whereas Batstone dared enter into the spirit of things, casting his dramas of genre-indifferent rhetorical performativity in the outward shell of a scriptwriter’s formatting , as he cooks up a narrative leading us from staged scenes of worlds talked into existence and on to settings for world-shattering debate in and as historiography, then out towards a finale vista of entire cultures manned by selves realized as roles; and Gunderson was (acting out) toying with his reanimation of Quintilian’s project as the realization of its own training by mounting his account as a fresh realisation of Quintilian’s training - with the didactic difference that the essay runs us lap after lap through explicitation of his quasi-oration’s ticking the grand manual’s boxes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".