Festive Sermons of Metropolitan Stefan Yavorsky. The Problem of Textual Criticism
Bibliographic record
Abstract
The creative history of Metropolitan Stefan Yaworsky’s homiletic heritage still remains unexplored. The hypothesis about the existence of an established author’s collection of festive sermons, put forward earlier, needed textual material to rely on. The study of the unique manuscript of the first quarter of the 18th century (The Russian State Historical Archive, f. 834, op. 2, No. 1592g) — the author’s draft autograph of sermons — and its comparison with later manuscripts of festive speeches allowed us to conclude that the author undertook a serious substantive, stylistic and lexical editing of the speeches included in the collection. A comparative textual analysis of the manuscripts suggested that the author’s revision of the original draft was carried out by Metropolitan Stefаn in the period from 1711 to 1718, in the course of preparing for publication a collection of the most significant victorial speeches of the first decade of the 18th century. In this regard, we come to the conclusion that Gabriel Buzhinsky, who collected and copied Yavorsky’s laudable sermons after his death (in 1722‒1726), was not the editor and compiler of this collection, but relied, apparently, on the versions of these texts already edited by the author. This is supported by the fact that there are no copies of autograph (No. 1592) in the manuscript tradition of the 18th century.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".