Bibliographic record
Abstract
This article considers how Solon recasts the traditional imagery of warfare and violence in order to bolster his persona as a “reconciler” (cf. Ath. Pol. 5.1–2) and peacemaker. Beginning with the fragments of his Salamis, I examine Solon’s skilled use of persona and emotion, building on the elegiac tradition of martial exhortation. Turning to fr. 4, I trace one of its most striking features, namely, the way it applies the language of epic warfare to stasis, and so suggests that the dichotomies of war versus peace, and enemy versus self, do not work in contemporary Athens. The manipulation of epic language and war imagery to support Solon’s role as reconciler is equally evident in fr. 5, and communicates Solon’s role as an outstanding and impartial protector, whose achievement has been to save the Athenians from unjust (i.e., internal) violence. In fr. 36, one of the most fascinating surviving examples of the political use of iambus in the archaic period, Solon foregrounds the damage to Athenian society caused by greed, debt, and enslavement for it and presents his unbiased reforms as having prevented civil war. By adapting epic imagery, Solon emphasizes his unfair treatment as an outsider, as he, the saviour of his community, is cast in the role of an antisocial animal. Finally, in fr. 37, Solon’s imagery is geared to arguing that the current conflict is best resolved not by violence but by peaceful agreement, a symbol of reconciliation embodied in Solon himself. Throughout my discussion, I focus on how Solon uses two strategies in particular to communicate the importance of his role: the first is the way he applies military language and metaphors to political situations and relationships in order to highlight the evils of stasis; the second is the way he recasts traditional imagery of warfare and violence to highlight his efforts, and his success, as a “reconciler” (διαλλακτής) of the warring parties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".