Bibliographic record
Abstract
In a world where song had an embedded social position, poetry had a vital role to play in constructing and confirming or destabilizing and destroying reputation and social standing. Though Pindar, for his own programmatic reasons, chooses to see poetry of praise and blame (reified for him in its most famous exponent, Archilochos) as fundamentally opposed (Pythian 2.54–6), they share certain needs. My interest here is poetry of blame. To express hostility at the most basic level, all that is needed is the terminology of personal abuse, an approach in which Alkaios is particularly inventive. The problem is that abuse is ultimately limited both aesthetically and in terms of impact. It can briefly unite speaker and audience at the expense of the person or group held up to ridicule. But it neither engages the imagination nor reaches out beyond the already committed. To be effective, blame, like praise, needs to speak from a position of authority if it is to achieve its pragmatic goal. This article looks at one means of generating authority, narrative. It looks at the construction of characters and events in straight storytelling and the use of paradigmatic narratives of the physical and animal worlds and examines the way in which archaic blame poetry uses these to construct a social, moral, natural, and religious order as a means of positioning speaker, audience, and target. This construction of authority is important for the vertical as well as the horizontal positioning of poetry of blame, in that it eases the movement of poetry and poetic reputation into the Panhellenic mainstream.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".