Bibliographic record
Abstract
This article discusses how Bertolt Brecht employed the ancient figure of Niobe as a model for several of his female characters. The examples examined here are his adaptation of Antigone, where the protagonist compares herself to Niobe, as in the classic Sophocles drama; the allusions by Polly Peachum in the Pirate Jenny song in the stage version of The Threepenny Opera; and the dialectical interaction between Mother Courage and her mute daughter Kattrin in Mother Courage and Her Children. Each such “likeness” encompasses a strong emotional involvement or empathy with the Trauer, or sorrow, of the ancient figure of Niobe, who was petrified for boasting of her many children to Leto, and whose offspring then killed all of her children. At the same time, Brecht also allows his characters to resist and even protest against their ancient female role model. On the basis of these examples, the article discusses Brecht’s modern version of the Trauerspiel, based on Walter Benjamin’s ideas; his development of the notion of the model and the model-book with Niobe as a model for the modelling process itself; and, finally, the importance of the notion of the Halt, a stop or interruption, for this modelling process. The article suggests that the close interdependence between Brecht’s oeuvre and the ideas of his close friend Walter Benjamin concerning the Trauerspiel reveals the modelling aspect of many of Brecht’s female characters as well as his multifaceted, critical approach to tragedy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".