Bibliographic record
Abstract
Like her Sophoclean predecessor, ps-Seneca’s Deianira kills her husband by mistake, sending him a robe that poisons him rather than restores his affections. Allusions to the words of vengeful protagonists—particularly Medea and Juno in Seneca’s Medea and Hercules Furens —complicate Deianira’s character with the models of deliberate killers. Deianira’s choice, to pursue anger or desire, is also a choice between the genres of tragedy and elegy, and in this respect ps-Seneca revisits the ironic mode distinctive of Ovid’s Heroides and of the Senecan tragedies that reappropriate their tragic material. Comme l’héroïne sophocléenne avant elle, la Déjanire du pseudo-Sénèque tue son mari par erreur, en lui envoyant une robe qui l’empoisonne au lieu de lui rendre son amour. Des allusions aux discours de protagonistes vengeurs – particulièrement Médée et Junon dans la Médée et l’ Hercule furieux de Sénèque – étoffent le personnage de Déjanire en l’associant à des modèles de tueurs volontaires. Le choix de Déjanire, de poursuivre sa colère ou son désir, est aussi un choix entre les genres tragique et élégiaque et, à cet égard, le pseudo-Sénèque revisite le mode ironique distinctif des Héroïdes d’Ovide et celui des tragédies de Sénèque qui se réapproprient leur matériel tragique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".