Trends in the repertoire of the Moscow Art Theater from 1917-1941
Bibliographic record
Abstract
When the Moscow Art Theater appeared in New York in 1924, it was the apostle of a new dramatic naturalism bent on depicting man's inner torment through an intense psychological probing. Some forty years later, one of the world's greatest art theaters occupies only an insignificant position in the world of art. Why is this so? It is the purpose of this dissertation to answer this question by a careful analysis of the trends in the repertoire of the Moscow Art Theater. An analysis of the repertoire reveals several major trends in the Theater's repertoire after the Revolution of 1917. These trends are revealed through performances in the following areas: (a) Russian classical plays; (b) Adaptations of Russian classical novels; (c) Translations of West European classical plays; (d) Mikhail Bulgakov and his controversial plays; (e) Modern Soviet drama. An analysis of carefully selected plays from each trend reveals how the Moscow Art Theater was systematically sapped of all its inner vitality and enslaved to a regime. The resultant loss of creative endeavour and the Theater's reliance on the Russian classics in the years following World War II confirms the indivisibility of art and freedom. Some of the Moscow Art Theater's trends were established before the Revolution of 1917. This dissertation therefore, begins by tracing the Theater's repertorial trends since the first performance in 1898.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".