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Record W4378471247 · doi:10.5430/jct.v12n3p198

Analysis of Modern Trends in Contemporary Choreographic Art: The Challenges of Digitalization

2023· article· en· W4378471247 on OpenAlexvenueno aff
Victor Lytvynenko, Ganna Perova, Nataliia Myroniuk, Andriy Krys, Natalia Koresandovych, А. М. Morozov

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsRealization (probability)MultimediaComputer scienceCognitionDigital artPsychologyArt

Abstract

fetched live from OpenAlex

The purpose of this article is to analyze current trends in choreographic art through the challenges posed by digitalization. The realization of the goal relies on the use of a number of approaches and methods integrated with pedagogical, art history, and social cognition. The results investigated digitalization as a way of creative choreographic communication, the role of synchronous and multimedia technologies in the system of digital learning, identified the main features of the multimedia environment, emphasized the use of video hosting and educational platforms for synchronous learning, drawing attention to the scheme of using educational materials during video classes. The conclusions indicate the importance of the further study of this problem (through the active development of digital technology) and emphasize the benefits of digitalization and its impact on choreographic art.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.009
Science and technology studies0.0020.010
Scholarly communication0.0070.010
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.056
GPT teacher head0.339
Teacher spread0.282 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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