Bibliographic record
Abstract
In this article Natalia Yakubova explores the interactions between theatre and literature as between an actress on one side and a man of letters on the other. The interactions discussed here came at a particular historical moment, when the transition from an actor-centred to a director-centred hierarchy was taking place, and the article deals with the letters written by actresses to the men who were playwrights and/or theoreticians of the new theatre: Eleonora Duse writing to Gabriele D’Annunzio, Vera Komissarzhevskaya to Valery Bryusov, Irena Solska to Jerzy Żuławski, and Gertrud Eysoldt to Hugo von Hofmannsthal. In each case study, attention is paid to the characteristics of the relationships between a given actress and a particular writer, their attitudes towards theatre reform, the way in which the actresses evaluated the literary status of their letter writing, and, significantly, the stylistic features of their writing. Natalia Yakubova is a senior researcher of the State Institute of Art Studies in Moscow, and was Marie Curie Fellow at the Institute of Literary Studies of the Polish Academy of Sciences between 2013 and 2015. She is author of O Witkacym (2010) and Teatr epokhi peremen v Polshe, Vengrii i Rossii 1990-ye–2010-ye (2014), and has published numerous articles in Russia, Poland, the USA, and Canada, among other countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".