Bibliographic record
Abstract
In book 23 of the Iliad , Ajax the son of Oïleus loses a foot race to Odysseus and ends up on the ground with a mouth full of cow dung. On receiving second prize (an ox, fittingly enough), he spits the dung from his mouth ( onthon apoptuōn ) and says, “Damn, the goddess tripped me up; she always stands like a mother by Odysseus and cares for him.” His audience of fellow warriors laughs happily ( hēdu gelassan ) at his remark, and the potential for shaming and conflict ends there (780-84). If some speech (such as that of the dilatory Nestor) flows like honey – being by implication persuasive, well composed, and fair-minded – other utterances fill the mouth with bile and splatter their targets. The one mode is lofty, judicious, and even perhaps divine; the other is agonistic, scrappy, and tied to the body and the earth. While this “praise and blame” model for understanding early speech performance is clearly too schematic, it serves as a necessary starting point for assessing archaic awareness of verbal tactics. The scene from the Iliad , although clearly not an extended speech, offers an example of jocular tactics that constitute a mild but telling form of abuse in an agonistic setting. The incident as a whole, trivial and brief as it is, points to a conundrum crucial in reconstructing awareness of speech techniques before the advent of prose writing and rhetorical treatises on speech composition – namely, why the happy laughter? Why is Ajax’s performance successful in defusing the incident and bonding with the audience?
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".