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Record W4381801892 · doi:10.32370/ia_2023_06_8

Ukrainian National Stylistics of Stage Vocabulary of Modern Children's Musical

2023· article· en· W4381801892 on OpenAlexvenueno aff
Liliana Belymenko

Bibliographic record

VenueIntellectual Archive · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Educational Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianFolkloreInterpretation (philosophy)MusicalAppealLiteratureVocabularyContext (archaeology)AestheticsArtStylisticsHistorySociologyLinguisticsVisual artsLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

The article is devoted to the issue of the reflection of Ukrainian national stylistics in the productions of children's musicals on the stages of domestic theaters in 2010-2020s. It was revealed that the appeal to the traditions of folk culture takes place in the context of the leading modern trends in stage art, thanks to the synthesis of which a unique color of the artistic image of the performance is achieved. The research revealed that the most important cultural and artistic principles of the Ukrainian national stylistics of the stage vocabulary of the modern children's musical are the reproduction of a living tradition and the author's directorial approaches to the interpretation of folklore forms. In some cases, the representation of the Ukrainian folk tradition in a historically authentic form is characteristic, but mostly the directors emphasize a certain stylization and conventionality of the national color - traditional elements are used to create the atmosphere of a folk tale in a modern interpretation. Characteristic stylistic coloring is achieved by using authentic material, folk chants, dances, elements of traditional Ukrainian decor, creating well-known folklore images.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.371
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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