Bibliographic record
Abstract
This is a survey of some of the problems surrounding imperial panegyric. It includes discussions of both the theory and practice of imperial praise. The evidence is derived from readings of Cicero, Quintilian, Pliny, the Panegyrici Latini, Menander Rhetor, and Julian the Apostate. Of particular interest is insincere speech that would be appreciated as insincere. What sort of hermeneutic process is best suited to texts that are politically consequential and yet relatively disconnected from any obligation to offer a faithful representation of concrete reality? We first look at epideictic as a genre. The next topic is imperial praise and its situation “beyond belief” as well as the self-positioning of a political subject who delivers such praise. This leads to a meditation on the exculpatory fictions that these speakers might tell themselves about their act. A cynical philosophy of Caesarism, its arbitrariness, and its constructedness abets these fictions. Julian the Apostate receives the most attention: he wrote about Caesars, he delivered extant panegyrics, and he is also the man addressed by still another panegyric. And in the end we find ourselves to be in a position to appreciate the way that power feeds off of insincerity and grows stronger in its presence.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".