Bibliographic record
Abstract
Professional doctors who became writers have been contributing greatly to the formation and development of Russian literature for more than two hundred years. The works of art created by doctors bore the imprint of a professional view of the body and soul of a human being, saturated the texts with medical realities and terms, which gave reason to talk about the medical text in Russian literature. At the same time, the erudition of doctors and scientists allowed them to use examples from fiction in their scientific papers, thus giving rise to a literary text in medicine. Literary and medical discourses interacted in works on the study of higher nervous activity, in psychiatry and psychology. Since the last quarter of the 20th century, the study of fiction has been regarded as an obligatory part of the academic discipline “Medical Humanities”. In the universities of the Russian Federation and abroad, special manuals and readers were created, which were based on fiction written by trained doctors such as A. P. Chekhov, M. A. Bulgakov, V. V. Veresaev, and also those writers who described complex physiological processes and conditions without special medical training – L. N. Tolstoy, F. M. Dostoevsky, A. I. Kuprin, A. I. Solzhenitsyn and others. The study of the elements of the poetics of a literary text, its plot, metaphors and symbols began to be used in the training of doctors in the course of narrative medicine. The article concludes the growing interpenetration and mutual influence of literary and medical discourses. In the first quarter of the 21st century, Russian literature turned out to be an indispensable resource for countering the dehumanization and commercialization of medicine, as well as preserve empathy and philanthropy in future doctors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".