The Life of Nil Stolobenskii in the Mid‑17th-Сentury Redaction: Attribution Issues, Research and Publication of the Text
Bibliographic record
Abstract
The history of the text of the Life of Nil Stolobenskii, despite a century and a half of scholarly interest, remains poorly studied. Among the unresolved issues are the attribution of the Second Redaction of the text (according to V. O. Kluchevskii’s terminology), the search for its literary sources, the identification and determination of the manuscripts, their textual analysis and the reconstruction of the history of the text. The author of the article analyzes the time and place of the creation of the Second Redaction of the Life of Nil Stolobenskii and its changes throughout the century of active distribution in manuscripts. The article identifies variants of the text found in the manuscripts from the middle of the 17th century to the third quarter of the 18th century, explains the reasons for their occurrence and establishes the relationship between them. The author points out the replication of compendia dedicated to Nil Stolobenskii in the 18th century and demonstrates the methods used by scribes of these books to work with Old Russian texts. The article also includes the publication of the 17th-century redaction of the Life of Nil Stolobenskii. According to the author of the article, this early copy has preserved the readings closest to the archetype.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".