Bibliographic record
Abstract
In Tolstoy’s time the phrase “popular literature” ( narodnaia literatura , “literature for or of the common people”) subsumed a variety of related products. It included, first, the literature of the people, especially the narrative forms of folklore: heroic songs, fairy tales, religious legends, and the like. Produced and orally perpetuated among the common people themselves, usually by quasi-professional performers, this category of popular literature assumed written or printed form only through the efforts of folklorists and other transcribers of its oral performance. Once such works became known it was not long before stylizations of them followed. These are clearly not “of the people” but imitate as closely as possible the spirit and forms of their models. Stylizations, particularly of the Russian fairy tale, are well represented in nineteenth-century Russian literature. Well-known examples are Pushkin’s Tale of the Fisherman and the Fish ( Skazka o rybake i rybke ), V. F. Odoevskii’s Moroz Ivanovich , S. T. Aksakov’s The Little Crimson Flower ( Alen'kii tsvetochek ), and P. P. Ershov’s The Little Humpbacked Horse ( Konek-gorbunok ). Tolstoy wrote many works, in particular his score or so of Stories for the People ( narodnye rasskazy ) which may be assigned to this category, but, as will appear below, not exclusively to it.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".