Learning to Love Medea: <i>The Hungry Woman, Mojada</i>, and the Pedagogy of Adaptation
Bibliographic record
Abstract
This essay proposes adaptation as a teaching strategy with which students can explore deeper understandings of any drama, using the case study of Medea by Euripides. By assigning comparative analysis of (translations of) Euripides’ play alongside two twenty-first century adaptations written by Chicanx playwrights Luis Alfaro (Mojada) and Cherríe Moraga (The Hungry Woman), I invite students to uncover dramaturgical discoveries through considerations of what I have called “the spirit of the source,” alongside analyses of the palimpsestuousness of the dramas and my spectator-based model of adapturgy. I argue that Medea’s traits and actions of unbecoming emerge as a thread connecting all versions of the eponymous plays, a theme rarely recognized by students without the revelations provided by adaptation pedagogy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".