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Festive Sermons of Metropolitan Stefan Yavorsky. The Problem of Textual Criticism

2024· book-chapter· W4416170987 on OpenAlexaboutno aff
Olga A. Krasheninnikova

Bibliographic record

VenueA.M. Gorky Institute of World Literature of the Russian Academy of Sciences eBooks · 2024
Typebook-chapter
Language
FieldArts and Humanities
TopicHistorical and Cultural Studies of Poland
Canadian institutionsnot available
Fundersnot available
KeywordsAutographQuarter (Canadian coin)Period (music)Metropolitan areaState (computer science)Textual criticism

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.027
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.258
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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