Bibliographic record
Abstract
Death features prominently in Tolstoy's artistic and intellectual universe. Tolstoy's very first work, Childhood , a semi-fictional account of scenes inspired by his childhood, relates the 10-year-old protagonist's first confrontation with death when his mother unexpectedly succumbs to an illness. Tolstoy was about 2 years old when his mother died, and 9 when his father followed her. This initial painful realization of human mortality clearly exerted a profound impact, prompting him to recreate the image of his mother in Childhood and to return again and again to the theme of death in his subsequent works. Both War and Peace and Anna Karenina contain celebrated death scenes unparalleled in world literature, but shorter works have been just as powerful, in particular “Three Deaths,” The Death of Ivan Ilych , and The Kreutzer Sonata . Notable death scenes are also scattered in many, if not most, of his other pieces, including the Sevastopol sketches, The Cossacks , and Hadji Murat . The prominence of death in Tolstoy's oeuvre can hardly be overstated, and so it is no surprise that Philippe Ariès discusses Tolstoy's treatment of death at length in his seminal The Hour of Death , a survey of the history of dying in Western civilization. Little Nikolenka, the protagonist and first-person narrator of Childhood , does not witness the final moments of his mother's life; he and his brother are led away from her deathbed, more out of concern for the dying mother than to spare the boys.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".