The Function of the <i>Deus ex Machina</i> in Euripidean Drama
Bibliographic record
Abstract
This dissertation explores Euripides' use of the deus ex machina device in his extant plays.While many scholars have discussed aspects of the deus ex machina my project explores the overall function not only of the deus ex machina within its play but also the function of two other aspects common to deus ex machina speeches: aitia and prophecy.I argue that deus ex machina interventions are not motivated by a problem in the plot that they must solve but instead they are used to connect the world of the play to the world of the audience through use of cult aitia and prophecy.In Chapter 1, I provide an analysis of Euripides' deus ex machina scenes in the Hippolytus, Andromache, Suppliants, Electra, Ion, Iphigenia in Tauris, Helen, Orestes, Bacchae, and Medea.I argue that in all but the Orestes the intervention does not have a major effect on the plot or characters and I identify certain trends in the function of deus ex machina scenes such as consolation, enhancing Athenian pride, and increasing experimentation in the deus ex machina's role in respect to the plot of the play and the wider world of myth.In Chapter 2, I examine cult aitia in Euripides' Hippolytus and Iphigenia in Tauris and argue that Euripides uses cult aitia in plays with strong religious or cultic themes in order to connect the world of the play with the world of the audience through ritual.I also argue against the idea that 71 Torrance (2013) and Zeitlin (2003) are particularly useful.72 This is not the same as intertextuality since multiple authors follow the same tradition and use of this tradition cannot be tracked to a specific author's usage but is more generalized.73 Euripides uses a version of Helen who goes to Troy in his Trojan Women, Orestes, and Hecuba (where Helen is referenced but does not appear).He uses a version of Helen who does not go to Troy in his Electra and Helen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".