Artistry and Irony in María de Zayas's <em>La Inocencia Castigada</em>
Bibliographic record
Abstract
By the time Maria de Zayas published her Desengaños amorosos, the honor scenario, due in large part to the dominance of the comedia and especially of Calderón, had taken on a number of characteristics that today seem inseparable from the familiar plotlines. A noblewoman, usually innocent of adultery but loved by another man, is believed for one reason or another to have dishonored her husband. He proceeds to verify that the affront has indeed been committed, and, upon (wrongly) coming to believe that his wife is guilty, undertakes to have her killed in secret so that his honor will not suffer from even the faintest whisper of scandal. The thematic conflicts of this scenario are subtle but powerful: truth versus appearance, justice versus revenge, love versus honor, the freedom of men versus the constraints on women. An extraordinary amount of comedia criticism has dealt specifically with what an audience is to make of this plotline: is it an affirmation of the oppression of women by a patriarchal system, or is it an ironic condemnation of the husband who, too quickly and therefore erroneously, punishes his wife for deeds she did not commit? Zayas's story, La inocencia castigada, poses anew a number of these questions, but her novela is utterly unlike any Calderonian plot.
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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 | 0.002 |
| 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 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".