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THE MOTIF OF THE “SEVEN SACRAMENTS” IN RUSSIAN ART OF THE 17TH CENTURY AND ITS BOOK SOURCE

2023· article· en· W4360917443 on OpenAlexaboutno aff
Liudmila B. Sukina

Bibliographic record

VenueRSUH/RGGU Bulletin Literary Theory Linguistics Cultural Studies Series · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsIconographyMotif (music)BaroquePassionIconPaintingArtLiteratureComposition (language)Quarter (Canadian coin)Art historyHistoryAestheticsPsychologyArchaeology

Abstract

fetched live from OpenAlex

The article deals with a rather rare for Russian art of the 17th century story of the Seven Sacraments. Its source was the engravings of the printing house of the Kyev Pechersk Lavra, which were among the book “projects” of the head of the Kyiv Metropolis of the Constantinople Orthodox Church, Petro Mohyla (1632–1647). A few images of that motif have long been known to researchers, but the origin of its variations has not been revealed. The issue is studied on the examples of two icons of the last quarter of the 17th century “Crucifixion with the Seven Sacraments”. Their distinguishing feature is the depicting the optional for all Christians sacrament of marriage in the foreground. The author of the article suggests that the creation of such an iconography was associated with the marriage of Tsar Feodor Alekseyevich. The development of baroque aesthetics also prompted artists to combine elements of different iconographic schemes in one composition and create a complicated version of the Life-Giving Tree with scenes of the seven Sacraments and the Passion of Jesus cycle. They used several literary and visual sources. The surviving samples of such a composition demonstrate the variability of its iconography and the creative nature of the work of Russian icon painters in the 17th century, who could create completely independent works on the same subject with pronounced individual characteristics.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
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 head0.015
GPT teacher head0.258
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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Same venueRSUH/RGGU Bulletin Literary Theory Linguistics Cultural Studies SeriesSame topicDiverse Scientific Research in UkraineFrench-language works237,207