Bibliographic record
Abstract
The article is devoted to the genre of arrangement of choral folk songs in the works of Ukrainian composer Mikhail Hayvoronsky (1892–1949). The relevance of the study has been inspired by the growing attention of art critics, folklorists and choral conductors to the little-known in Ukraine works of diaspora composers. The purpose of the article is to draw attention and get acquainted with Mikhail Haivoronsky’s cultural heritage as one of the outstanding Ukrainian living in the USA. The works of the composer were studied by diaspora musicologists (Vasyl Vytvitsky, Roman Prydatkevich, Antin Rudnytsky, Fedir Steshko) as well as Ukrainian experts (Boris Kudryk, Ksenia Kolessa).It was emphasized that the folkloristic work of famous ethnomusicologists, folklore collections and correspondence with famous folk music experts have provided the foundation for the work of Mikhail Haivoronsky. The composer has worked out folklore samples of the representatives of various ethnographic regions of Ukraine as well as Slavic (Belarusian, Croatian), American and Canadian peoples.Mikhail Haivoronsky’s innovations in the field of arrangements of folk song for choral performance find expression in the establishments of the choral course, precedence in Hutsul folklore, appliance of sound imaging (imitation of church bells in carols), development of Ukrainian national music style, and formation of the individual composer style.The high artistic level of Mihail Haivoronsky’s arrangements confirms their foregrounding in the performing practice of Ukrainian and Belarusian choirs and in the publications in foreign collected works. His evolutionary development as a composer and the importance of his creative contribution to the treasury of Ukrainian choral music has been emphasized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".