Bibliographic record
Abstract
Born in St.Petersburg in 1891, Michael Aleksandrovich Chekhov died 64 years later in the United States (1955 in Los Angeles). A nephew of the playwright Anton Chekhov and a member of the Moscow Art Theatre’s First Studio where the Stanislavsky system was forged, Chekhov had a celebrated acting career in Moscow. The Russian Revolution of 1917 and evolving communist cultural policy, however, forced him to leave his homeland. As an exile in Germany, France, Latvia, Lithuania England, New York and Los Angeles, he established a series of acting studios in which he tried to bring to artists in other countries and in other parts of the world new insights into the system developed by his own master, Konstantin Stanislavsky, along with a range of approaches to acting that was clearly his own. The adventures he had communicating this Russian style of work in English to artists working with him from across Europe, the United States, Canada and from as far away as Australia at a time when the Stanislavsky system was just becoming known was a source of linguistic frustration for him over the years as well as a source of linguistic frustration. What follows is a look at Michael Chekhov’s life and work across these many borders written by Chekhov specialist Liisa Byckling of Finland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".