Bibliographic record
Abstract
Seneca, in his tragedy Phaedra, created an elegiac character using, among other elegiac conventions, the amorous hunting. His Phaedra turns into an aggressive erotic predator who wants to “hunt” Hippolytus whom she is in love with. The prologue of Phaedra connects the play with elegiac poetry through the extensive use of venery description, because it highlights Hippolytus’ attitude to love: the young man sees the forest as a place of reclusive solitude where he can hide from frenetic passion. The prologue to Phaedra is also important from a spatial point of view, for Seneca associates his two main characters with a fundamental difference in locale that recalls the roman elegiac paraclausithyron, where the lover tries, without success, to penetrate into his beloved’s intimate space, the house. Furthermore, Seneca reverses the relationship between the lovers: Hippolytus becomes the beloved, Phaedra, the lover, thus inverting the gender roles of normal erotic elegy. At the same time, he amplifies this convention, making it the main theme of his tragedy, for Phaedra has a fundamental impact on the play’s action through her desperate attempts to conquer her stepson. Roman love elegy often associates the lover, the feeble man, with the hunter, while representing the beloved, the dominant woman, as his prey. Seneca goes further, because Hippolytus, the true hunter, becomes the erotic prey, while the female character takes on the role of the erotic predator. In this way, Seneca justifies the reversal of the male and the female characters’ roles in his use of the elegiac theme of hunting.
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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 | 0.001 | 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 | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".