Bibliographic record
Abstract
Tolstoy stands before us as a monumental chronicler of the human experience throughout every stage of life as individual, as member of a complex society, as seeker after truth through philosophy, religion, and art. But the other animals of the biosphere are also essential to Tolstoy's vision; man is, as characters like Pierre or Levin realize full well, merely one link in the great chain of being. The animal kingdom permeates Tolstoy's written world – as it did his actual existence – in myriad and often contradictory ways. Animals helped shape his views of art, death, happiness, life, history, causality, order and chaos, friendship, relations between men and women, morality, and philosophy – indeed all the preoccupations that alternately perplexed Tolstoy, drove him to despair, and gave his life meaning. Animals figure in countless permutations but are always close to the center of his ruminations. This chapter begins by tentatively exploring ways in which Tolstoy's ideological views on animals mesh with those of some philosophers and writers who are interested in these questions today. The question of animal rights and the degree to which animals resemble human beings has reengaged philosophers and scientists in the past several decades, perhaps as a result of what has been learned about animal behaviors and the animal brain. My primary interest, however, is in Tolstoy's representations of animals in his fiction. Animals are essential to Tolstoy's framing of aesthetic, moral, social, personal, and philosophic questions.
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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 | 0.000 | 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 | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".