Bibliographic record
Abstract
At the end of Sevastopol in May , Tolstoy makes a famous claim, central to his fiction and startling in its simplicity and boldness: “The hero of my tale, whom I love with all the strength of my soul, whom I have attempted to depict in all of his beauty, and who was, is and will always be sublime, is the truth.” In notes to War and Peace a decade or so later, he wrote: “I was afraid that the necessity to describe the significant figures of 1812 would force me to be governed by historical documents rather than the truth.” But what did Tolstoy mean by “truth” in works of fiction which have, since Aristotle, been understood to describe not what is but what might be? Given that both works contain this Tolstoyan truth, it makes sense to search for it in their intersection. At first, this approach may seem unpromising, however. Sevastopol in May , a feuilleton, focuses on the day-to-day life of a few “randomly chosen” soldiers during a short period of time in an enclosed space. War and Peace , set entirely in the past, sprawls over multiple characters and huge chunks of time and space. In other ways, however, Sevastopol Sketches and War and Peace are quite similar. Both are constructed of “real” material taken from life (and especially from Tolstoy’s biography), while that same material is fitted into a context that disguises its provenance. Tolstoy was in Sevastopol.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".