Andreyev’s “Mysl’” (“Thought”) as an Object of Literary and Psychiatric Analysis
Bibliographic record
Abstract
The article analyses L.N. Andreyev’s short story “Mysl’” (“Thought”, 1902) both on the formal-compositional level and on that of its ‘clinical’ history. After having premeditatedly murdered his friend Savelov, Doctor Kerzhentsev, the protagonist, asks himself and the psychiatrists carrying out his clinical evaluation the question: when he killed Savelov, was he crazy or did he simulate madness to escape justice? The question remains unanswered both in the story and in the play with the same title (1914). Between 1903 and 1914, numerous, often eminent, scholars — psychiatrists and neurologists — tried to answer this question, but were unable to come to any definitive conclusion. In the 1980s, an Italian Jungian psychiatrist explained why it was impossible to subject a literary character to an objective psychiatric analysis, and in the early 2000s a Canadian literary critic turned the problem on its head, stating that it was Andreyev who was mentally ill; it was not Kerzhentsev who simulated madness, but rather his author who simulated sanity. Kerzhentsev’s question therefore remains unanswered, but over time “Mysl’” has also been enriched with a historical and sociological value, becoming representative of the Russian cultural and social atmosphere of the early 1900s, the years defined as the “Little Apocalypse” and the “Nervous Century”.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".