Moralizing Strategies in Early Greek Poetry
Bibliographic record
Abstract
The articles collected in this volume were originally presented at a conference held at Memorial University, St John's, Newfoundland, in July 2015. \n \nMoralizing, in the sense both of didactic advice and of reflection on received wisdom, is a prevalent feature of ancient Greek literature. Didactic poetry was a long-established genre, first known to us from Hesiod, though with much older roots, but elements of didacticism are important to small-scale non-hexameter poetry, whose performance function is often explicitly to guide or advise its audience. Yet moralizing strategies are found in poetry whose aim is not simply to tell its audience how to live their lives, and a core aim of the conference on which this volume is based was to explore ways in which early Greek poetry uses implicit moralizing within a broader poetic framework. In other words, we are interested in exploring how moralizing is embedded in poetry whose overt goal appears to be something different (including, for example, entertainment, storytelling, or the expression of blame). The term “moralizing” may also imply a straightforward lesson for the audience, yet early Greek poetry often complicates any such “message” even as it gives it, and a further aim of the collection is to think about the status of the moral advice embedded in the performance. To what extent is direct advice questioned or undermined? How far can the audience trust the narrator and his or her perspective? \n \nThe articles in this collection address this question from different perspectives, and using different texts, but coalesce around a group of core themes and questions. One area of particular interest is the difference between explicit and implicit moralizing, and how the advice given by the two can be reconciled. Another overarching theme is the relationship between moralizing in lyric and how it is used in Homer and Hesiod. Lyric poetry (including elegy and iambus) frequently positions itself as in dialogue with the epic tradition, and borrows from and recasts epic motifs for its own purposes. Yet the performance context and imagined audience of lyric may differ from those of epic, and the strategies adopted by the poets vary accordingly. Many of the articles discuss beast fable and animal imagery, contesting the commonly-held idea (first proposed by Karl Meuli) that a fable does not carry a universal truth but rather a specific one to suit the context. Finally, questions of persona and poetic biography, and of the audience and performance context, play an important role in understanding how morality is disseminated and received.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.022 | 0.019 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.019 |
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; both teacher heads agree on what is shown here.
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".